Running head: IMPACT OF EHR SYSTEMS1
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Analyzing the Impact of Implementing EHR Systems on Healthcare Quality, Patient Safety,
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and Efficiency
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Pranitha Pittala1
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Harrisburg University of Science and Technology
Author Note
Correspondence concerning this article should be addressed to Pranitha Pittala, Postal
address. E-mail: PPittala@my.harrisburgu.edu
IMPACT OF EHR SYSTEMS
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Abstract
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This proposal outlines an investigation into the ramifications of implementing Electronic
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Health Record (EHR) systems regarding delivering high quality healthcare; specifically
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analyzing effects pertaining to patient safety standards, documentation accuracy
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completeness and timeliness as well as overall efficiency measures. Our methodology entails
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collecting information through secondary data sources such as medical and administrative
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databases alongside surveys conducted regarding staff experiences with post implementation
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periods – including any potential benefits or drawbacks experienced. Our approach includes
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statistical techniques such as regression analysis or hypothesis testing which will offer insights
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into possible relationships between EHR implementation and healthcare outcomes. This
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study has the potential to add significant value to current knowledge bases within health
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informatics and help in supporting decision makers worldwide towards EHR adoption with
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informed understandings of effects on various aspects of care delivery. This research adds to
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the body of healthcare informatics literature by drawing on publicly accessible secondary
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data to examine the influence of EHR systems on vital components of healthcare delivery.
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Keywords: Electronic Healthcare Record Systems, Healthcare Delivery, Patient Safety,
Healthcare Quality, Healthcare Efficiency.
Word count: 3571
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Analyzing the Impact of Implementing EHR Systems on Healthcare Quality, Patient Safety,
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and Efficiency
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By offering digital platforms for securely storing, managing, and distributing patient
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data, the implementation of EHR systems has radically altered the healthcare context.
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Improved documentation accuracy, augmented patient safety, and augmented operational
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efficiency are some potential advantages of EHR systems (Lavin et al., 2015). As more
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healthcare organizations adopt EHR systems, it is vital to scrutinize their influence on
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healthcare quality, patient safety, and efficiency. Gaining insights into these effects is crucial
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for smart decision-making, facilitating superior healthcare outcomes, and optimizing patient
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care. The shift from conventional paper-based medical records to electronic health record
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systems has been motivated by the acknowledgement that EHR systems possess the
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capability to transform the delivery and management of healthcare (Aguirre et al., 2019).
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The process of digitizing patient records enables healthcare practitioners to promptly retrieve
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extensive patient data, thereby enhancing the precision of diagnoses, treatment choices, and
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care management (Wheatley, 2013). Electronic Health Record systems offer advantages that
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go beyond individual patient visits. They facilitate the smooth exchange of data across
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various healthcare environments, guaranteeing consistency of care and minimizing the
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likelihood of medical errors that may result from inadequate or disjointed information. The
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primary rationale behind the analysis of the influence of Electronic Health Record systems
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on healthcare quality is to guarantee precise and comprehensive documentation. The absence
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of precise or comprehensive documentation can result in grave implications for the provision
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of healthcare to patients, such as medical inaccuracies, postponed diagnoses, and jeopardized
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patient welfare. According to Evans (2016), Electronic Health Record systems have the
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potential to improve documentation accuracy by reducing errors resulting from illegible
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handwriting, lost or misplaced records, and incomplete data. Examining how the adoption of
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EHRs has affected documentation accuracy enables healthcare providers to better target
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their resources and develop strategies for delivering better care. In addition, the promptness
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of documentation plays a crucial role in guaranteeing effective and synchronized provision of
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care. The prompt and efficient retrieval of patient data is a critical component for healthcare
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practitioners to make well-informed judgments, administer suitable therapies, and effectively
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track patient advancement (Adane, 2019). Electronic Health Record systems facilitate
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instantaneous retrieval and updating of patient data, thereby enabling healthcare providers
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to access real-time information (Wheatley, 2013). The implementation of technology in
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healthcare not only optimizes the speed and effectiveness of medical procedures, but also
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facilitates expeditious correspondence between healthcare professionals, resulting in
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enhanced patient results and diminished healthcare expenditures. The adoption of electronic
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health record systems presents favorable prospects for augmenting patient safety and
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mitigating the incidence of avoidable harm. Electronic health record systems offer a range of
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features, including medication reconciliation, tools for decision-support, and clinical alerts,
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that can assist medical professionals in making more educated and safer decisions
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(Lindén-Lahti et al., 2022). Health care facilities may determine areas where electronic
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health record systems have significantly improved patient safety by studying the impact of
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EHR deployment on medication mistakes, negative outcomes, and hospital-acquired
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infections. They may then create plans to tackle any unresolved problems or risks.
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Influence Of Electronic Health Record Systems on Healthcare Workflow and
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Productivity The effective adoption of Electronic Health Record systems within healthcare
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establishments holds the capacity to optimize operational processes, elevate output, and
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augment overall efficacy. Electronic Health Record systems provide a range of capabilities
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and characteristics that mechanize and digitalize diverse administrative and clinical duties,
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thereby diminishing the dependence on manual and paper-oriented procedures (Aguirre et
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al., 2019). Comprehending the effects of electronic health record implementation on
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healthcare processes and productivity is of paramount importance for healthcare entities
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seeking to enhance their operational efficiency and provide superior patient care. Optimizing
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Administrative Processes Appointment scheduling, invoicing, and the filing of insurance
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claims may all be handled more efficiently with the help of an electronic health record
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system. These systems offer centralized databases for the storage of patient data, thereby
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negating the need for input of data manually and diminishing the probability of errors
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(Ehrenstein et al., 2018). The automation of administrative tasks within healthcare
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organizations has the potential to optimize workflows, enhance resource allocation, and boost
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productivity. Electronic Health Record systems facilitate the process of appointment
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scheduling by enabling healthcare providers to access and review available time slots, reserve
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appointments, and dispatch automated notifications to patients. The implementation of an
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automated appointment scheduling system alleviates the task of manual schedule
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management and mitigates the potential for scheduling conflicts or overlooked appointments
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(Howard et al., 2020). Furthermore, faster and accurate billing procedures are made possible
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by the connection of EHR systems with billing and claims processing activities. Billing forms
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automatically include patient information, saving time and effort by eliminating the need for
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human data input. EHR systems also offer capabilities for verifying insurance coverage and
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submitting claims, minimizing paperwork and speeding up payment procedures.
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Optimization of Data Retrieval and Information Sharing Processes EHR systems facilitate
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the expedient retrieval of data and the sharing of information among medical professionals,
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resulting in increased productivity and work efficiency. In the past, healthcare practitioners
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were dependent on tangible paper charts or the transmission of documents via facsimile for
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the purpose of exchanging information (Evans, 2016). Electronic health record systems make
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patient information easily available at the point of treatment, doing away with the need for
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manual searches while also cutting down on time wasted. By utilizing modern technology,
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medical practitioners are able to swiftly retrieve comprehensive medical records, laboratory
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findings, and radiographic assessments, facilitating prompt clinical decision-making and
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optimizing operational productivity. Electronic health record systems provide extensive
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search functionalities, enabling healthcare professionals to swiftly access particular data or
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monitor changes in patient status throughout the course of treatment (Quinn et al., 2019).
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The prompt availability of patient information empowers healthcare practitioners to make
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informed judgments, devise therapeutic strategies, and deliver prompt interventions. In
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addition, electronic health record systems facilitate the secure exchange of patient data
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among various healthcare settings, thereby promoting efficient care coordination and
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mitigating communication barriers. In the past, the exchange of patient records among
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healthcare providers necessitated laborious procedures, such as transmitting physical records
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via facsimile or postal mail (Ajami & Bagheri-Tadi, 2013). Electronic health records systems
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facilitate the electronic interchange of patient data, making it accessible to authorized
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healthcare professionals (Ajami & Bagheri-Tadi, 2013). The optimization of information
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sharing facilitates effective communication, minimizes redundant testing, and amplifies
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workflow efficiency in diverse healthcare environments, including medical centers, pharmacies,
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and, hospitals. Workflow Integration and Clinical Decision Support Electronic Health Record
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systems frequently integrate clinical decision support mechanisms that offer healthcare
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providers real-time notifications, prompts, and evidence-based recommendations (Lavin et
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al., 2015). The utilization of these tools facilitates the process of clinical decision-making and
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increases the efficacy of workflow (Sutton et al., 2020). Medication-related adverse events
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may be avoided, for instance, if electronic health record systems are used to highlight the
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possibility of drug interactions, allergies, or dosing mistakes. The EHR’s built-in clinical
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decision support capabilities examine the patient’s medical history, including their
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prescriptions, allergies, and diseases, and then provide warnings or suggestions based on
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these analyses. These cautions might be anything from reminders about preventative
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screenings or vaccines to warnings about interactions between medications (Sutton et al.,
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2020). By incorporating decision support tools into electronic health record systems, medical
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professionals are able to make choices that are better informed, hence lowering the risk of
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making errors and increasing the possibility that patients will be safe. Electronic Health
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Record systems have the capability to enhance workflow integration by establishing
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connections among different departments and healthcare practitioners who are engaged in
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providing patient care. When a physician requests laboratory tests using an EHR system, for
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example, the request is transmitted electronically to the laboratory. This eliminates the
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requirement for manual order input and reduces the amount of time it takes to complete the
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tests (Wheatley, 2013). The findings are subsequently sent in a computerized way to the
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EHR system, where they are made immediately available to the physician who placed the
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prescription. The integration of various care teams results in a reduction of time and effort
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needed for coordination and communication, leading to streamlined workflows and increased
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productivity (Wheatley, 2013). Workforce Productivity and Job Satisfaction Healthcare
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workers’ levels of job satisfaction and productivity may change with the introduction of
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electronic health record systems. Medical professionals generally report more work
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satisfaction and less administrative stress after mastering electronic health records, despite
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the fact that the switch from paper-based systems sometimes need training and adjustment
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periods (Ajami, 2013). Electronic health record systems have the potential to streamline
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documentation procedures through the provision of drop-down menus, templates, and voice
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recognition functionalities, thereby facilitating accelerated and precise documentation. As
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opposed to using handwritten or transcribed information, healthcare personnel may
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immediately enter patient interactions into the EHR system, eliminating the risk of mistakes
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caused by handwriting that is indecipherable or missing information (Evans, 2016). In
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addition, electronic health record systems come with features like copy-and-paste and
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auto-fill, both of which help to simplify paperwork even more and save time. In addition,
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electronic health record systems allow for quicker data retrieval, since they do away with the
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need to physically find documents or sift through piles of paper charts. The ability for
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medical professionals to swiftly access patient information, go through patients’ medical
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histories, and evaluate test results enables them to make more effective clinical decisions
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(Manca, 2015). The provision of instantaneous data additionally facilitates enhanced
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communication and collaboration amongst healthcare teams, thereby promoting
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synchronized patient care. The aforementioned factors are conducive to enhanced job
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satisfaction, as healthcare professionals are able to allocate more attention towards patient
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care and minimize the amount of time dedicated to administrative duties. The alleviation of
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paperwork and administrative duties enables healthcare practitioners to devote additional
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time and focus to face-to-face patient engagements, improving their sense of fulfillment and
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purpose (Barello et al., 2015). Improved satisfaction with work, in turn, may boost employee
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productivity, resulting in higher-quality treatment for patients and improved medical results.
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Influence of Electronic Health Record Systems on Patient Safety and Quality of Care The
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adoption of Electronic Health Record systems within healthcare institutions has resulted in
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notable progressions in patient safety and the general standard of care. EHR systems include
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features and functions that help with timely and accurate documentation, better drug
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control, fewer adverse events, and improved provider communication (Evans, 2016).
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Comprehending the effects of EHR implementation on the safety of patients and care quality
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is imperative for healthcare institutions seeking to improve patient outcomes and establish a
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safe environment for care. Improved Medication Administration EHR systems are essential
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for enhancing drug administration and minimizing pharmaceutical errors, which may have
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detrimental effects on patient safety. Healthcare practitioners may electronically prescribe
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pharmaceuticals using EHR systems, eliminating the dependence on handwritten
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prescriptions, which are prone to mistakes (Porterfield, et al., 2015). The incorporation of
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e-prescribing functionalities in electronic health record systems is accompanied by inherent
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safety protocols, including drug-drug interaction notifications, allergy advisories, and dosage
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suggestions, that facilitate the informed decision-making of healthcare practitioners in the
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course of prescribing medication. In addition, electronic health record systems enable precise
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reconciliation of medication through the provision of a comprehensive overview of a patient’s
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medication history, encompassing present medications, previous prescription drugs, and drug
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allergies (Gildon et al., 2019). This data assists healthcare professionals in verifying the
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suitability of prescribed medications, preventing redundancy, and reducing the likelihood of
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negative drug responses. EHR systems improve patient safety and the provision of
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high-quality care by lowering medication mistakes and managing medications better.
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Reduced Adverse Events and Infections Acquired in Hospitals The adoption of electronic
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health record systems has demonstrated potential in mitigating the incidence of unfavorable
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incidents and nosocomial infections, thereby enhancing patient safety. Electronic Health
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Record systems facilitate healthcare providers’ access to current patient data, such as
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laboratory findings, diagnostic assessments, and vital signs, during the provision of care
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(Ehrenstein et al., 2019). The swift availability of crucial patient information enables
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healthcare practitioners to make well-informed decisions rapidly, resulting in the prompt
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detection and intervention of declining patient conditions. Furthermore, EHR systems
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include clinical decision support technologies that give healthcare practitioners with real-time
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alerts and reminders. These alerts may let medical professionals know about possible safety
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hazards including allergies, contraindications, or unexpected test results, allowing for prompt
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attention and the right course of action (Lavin et al., 2015). The integration of clinical
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decision support into electronic health record systems enables healthcare organizations to
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enhance patient safety by reducing the number of adverse occurrences that may have been
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avoided. EHR systems can enhance care coordination by improving communication between
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healthcare providers. Healthcare providers have the ability to electronically exchange patient
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data, such as treatment plans, notes on progress, and summaries of discharge, to facilitate
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comprehensive and collaborative care delivery involving all pertinent stakeholders
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(Lindén-Lahti et al., 2022). This all-encompassing and well-coordinated approach to patient
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care decreases the risk of making mistakes, boosts patient safety, and increases the quality of
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treatment as a whole. This research uses electronic healthcare records datasets from Kaggle,
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the UCI Machine Learning Repository, and the Harvard Dataverse databases to examine the
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effects of deploying Electronic Health Record systems on the quality of healthcare, safety for
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patients, and efficiency. The EHR datasets should contain an exhaustive compilation of
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electronic medical records from a variety of healthcare facilities. It consists of the patient’s
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demographic information as well as their medical diagnoses, treatments, test findings,
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prescription records, and any other pertinent information that has been documented inside
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EHR systems. This dataset is a valuable source of data that may be used to investigate the
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links between the implementation of EHR systems and the results of healthcare. Research
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Questions: Using the datasets obtained, this study aims to examine the following questions:
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RQ1. How does the implementation of EHR systems affect the overall quality of
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healthcare services provided, including the accuracy, completeness, and timeliness of
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documentation?
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RQ2. What are the specific improvements in patient safety observed after the adoption
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of EHR systems, such as reductions in medication errors, adverse events, or hospital-acquired
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infections?
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RQ3. How does the implementation of EHR systems impact healthcare efficiency
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measures, such as the time spent on documentation, patient wait times, or resource
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utilization?
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RQ4. What are the perceived benefits and drawbacks reported by healthcare providers
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following the implementation of EHR systems, and how do they influence workflow,
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productivity, and job satisfaction?
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H1: There is a positive correlation between the implementation of Electronic Health
Record systems in healthcare organizations and healthcare quality.
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H2: The implementation of EHR systems improves patient safety in healthcare settings.
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H3: The adoption of EHR systems in healthcare delivery positively impacts efficiency
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by streamlining administrative tasks, reducing paperwork, and eliminating duplicate data
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entry.
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Methods
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Through use of descriptive analysis, inferential statistics and regression modelling
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methods, this research seeks an elaborate understanding concerning how implementing
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Electronic Health Record impacts various aspects within healthcare delivery such as quality,
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patient safety, and efficiency. Datasets derived from sources like Kaggle, Harvard Dataverse,
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and UCI Machine Learning Repository provide essential details concerning patient histories
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inclusive but not limited to medication records, laboratory results and other relevant details.
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Electronic health records (EHRs) were implemented to improve the quality of care and
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patient outcomes. This study assessed the relationship between EHR adoption and patient
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outcomes. We performed an observational study using State Inpatient Databases linked to
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the American Hospital Association survey, 2011. This study aimed to determine whether
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hospitals with fully implemented EHR systems had better patient care than hospitals with
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partial or no implemented EHR systems after controlling for other important patient and
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hospital characteristics. This study used cross-sectional analyses, comparing patient
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outcomes (mortality rates, readmission rates, complications) among hospitals with no EHR,
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partial EHR, and full HER. This data was used to test the hypothesis that. Hypothesis 2:
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The implementation of EHR systems improves patient safety in healthcare settings.
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After that, I also needed to confirm the hypothesis that
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Hypothesis 1: There is a positive correlation between the implementation of Electronic
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Health Record systems in healthcare organizations and healthcare quality.
Hypothesis 3: The adoption of EHR systems in healthcare delivery positively impacts
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efficiency by streamlining administrative tasks, reducing paperwork, and eliminating
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duplicate data entry.
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To evaluate EHR systems’ usage and effectiveness in enhancing safety, quality, and
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efficiency. In this case, there was no similar dataset. However, I found enough data from a
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secondary online publication conducted in 2011 by Yanamadala et al., 2016.
We utilized discharge data from the 2011 State Inpatient databases (SID), Healthcare
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Cost and Utilization Project (HCUP), and Agency for Healthcare Research and Quality
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accessible on Kaggle, Harvard Dataverse, and UCI Machine Learning Repository. SID is an
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all-capture state database that allows the linkage of Patients over time and contains
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information on patient characteristics, primary and secondary diagnoses, and procedures
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received. Patient safety indicators (PSI) are based on ICD-9-CM codes and Medicare
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severity diagnosis-related groups (DRGs), with specific inclusion and exclusion criteria
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determined by the Agency for Healthcare Research and Quality (AHRQ). Each PSI includes
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a unique denominator, numerator, and set of risk adjustors.
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Data
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The present investigation will acquire the requisite data from the designated data set
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from the EHR, which is accessible on Kaggle, Harvard Dataverse, and UCI Machine
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Learning Repository. The electronic healthcare records in this collection, which come from
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multiple healthcare institutions, include in-depth data on demographics of patients, health
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diagnoses, treatments, test findings, prescription records, and other pertinent factors. The
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data set will include a large number of patient records, enabling for a thorough examination
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of the effect of EHR deployment on healthcare quality, safety for patients, and efficiency.
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We performed an observational study using State Inpatient Databases linked to the
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American Hospital Association survey, 2011. This study aimed to determine whether
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hospitals with fully implemented EHR systems had better patient care than hospitals with
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partial or no implemented EHR systems after controlling for other important patient and
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hospital characteristics.
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This study assessed the relationship between EHR adoption and patient outcomes. We
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performed an observational study using State Inpatient Databases linked to the American
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Hospital Association survey, 2011. This study aimed to determine whether hospitals with
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fully implemented EHR systems had better patient care than hospitals with partial or no
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implemented EHR systems after controlling for other important patient and hospital
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characteristics. This study used cross-sectional analyses, comparing patient outcomes
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(mortality rates, readmission rates, complications) among hospitals with no EHR, partial
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EHR, and full EHR. The data cleaning process involved collecting, evaluating, formatting,
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assessing inconsistencies and errors in the data set.
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Data set 1 Description The data used in the study consisted of information on surgical
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and medical patients treated at various hospitals. The study included a total of 137,162
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surgical patients and 311,605 medical patients. The hospitals were categorized based on their
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level of electronic health record (EHR) utilization. The categories included hospitals with no
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EHR, hospitals with partial EHR, and hospitals with full EHR. The percentages of patients
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treated at each type of hospital were reported. Patient characteristics, such as demographics
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and comorbidities, were also recorded. These characteristics were considered as independent
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variables in the statistical analyses. The dependent variables in the study were mortality,
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readmissions, and complications measured by Patient Safety Indicators (PSIs). These
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variables were used to assess the quality of care provided to the patients. The study
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employed multiple regression analysis and difference-in-differences analyses to investigate the
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relationship between EHR utilization and quality of care. The statistical analyses aimed to
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determine if there were statistically significant differences in patient outcomes based on the
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level of EHR implementation in the hospitals. The statistical analyses were performed using
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software packages, specifically STATA version 13.0 and SAS version 9.3. The specific
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statistical models and methods used were not explicitly mentioned in the excerpt provided.
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To evaluate EHR systems’ usage and effectiveness in enhancing safety, quality, and
efficiency. In this case, there was no similar data set. However, I found enough data from a
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secondary online publication conducted in 2011 by Yanamadala et al., 2016. Data set 2
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Description The data evaluated had a total number of patients assessed amounting to
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137,162, whereby those served under the partial EHR system accounted for 55.88%
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(Yanamadala et al., 2016). Besides, the number of patients served under full EHR was
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relatively lower than in the case of partial EHR applications. Besides, the no EHR case had
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the least number of patients accounting for 2.7% of the total patients. We assessed the
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patients on basis of who had undergone the procedures like Coronary Artery Bypass
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Graft(CABG), Abdominal Aortic Aneurysm Repair(AAA), Endovascular Aneurysm
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Repair(EVAR), Lateral Orbitotomy with Bony decompression(LOBE), Colonoscopy(COLO),
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and Total Hip Replacement(HIP) procedures under No EHR, Partial EHR, and Full EHR.
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Predictors
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We utilized discharge data from the 2011 State Inpatient databases (SID), Healthcare
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Cost and Utilization Project (HCUP), and Agency for Healthcare Research and Quality
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accessible on Kaggle, Harvard Dataverse, and UCI Machine Learning Repository. SID is an
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all-capture state database that allows the linkage of Patients over time and contains
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information on patient characteristics, primary and secondary diagnoses, and procedures
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received. Patient safety indicators (PSI) are based on ICD-9-CM codes and Medicare
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severity diagnosis-related groups (DRGs), with specific inclusion and exclusion criteria
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determined by the Agency for Healthcare Research and Quality (AHRQ). Each PSI includes
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a unique denominator, numerator, and set of risk adjustors.
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Outcome
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The quality of medical treatment, the safety of patients, and how efficiently resources
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are used are going to be the key focus measures of this study. The evaluation of these
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outcomes will be conducted through the utilization of diverse indicators, including but not
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limited to preciseness, comprehensiveness, and documentation timeliness, to gauge the
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quality of care delivered. Medication mistakes, adverse events, and hospital-acquired
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infections will be examined to determine patient safety improvements. The efficiency metrics
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will prioritize factors such as the duration of documentation, the duration of patient wait
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times, and the utilization of resources. Furthermore, secondary outcome measures like the
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perceived advantages and negative aspects indicated by healthcare personnel, such as
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productivity, workflow, and job satisfaction, will be taken into consideration.
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Data Analytic Plan
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For the purpose of this investigation, we will do an analysis of the data that combines
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descriptive analysis, and regression modeling. The utilization of descriptive statistics is
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intended to provide a summary of the dataset’s features, encompassing the variables
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distribution and measures of summary. Regression modeling, which accounts for possible
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confounding variables, will be used to evaluate the relationships between the determinant
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variables (EHR implementation factors) and the variables that determine the outcome. In
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order to guarantee the reliability of the outcomes, proper statistical methods will be used,
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taking into account the characteristics of the variables and the inquiries posed by the study.
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The potential for EHR implementation’s effects to vary among populations or environments
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may be investigated using sensitivity and subgroup studies. A determination will be made
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on the statistical significance of the relationships as well as the size of the effects, and any
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assumptions or limitations regarding the research will be evaluated and explained.
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The dataset analysis can be summarized as follows:
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Descriptive Statistics: The study used univariate regression analysis to develop
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descriptive statistics.
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Cross-Sectional Analyses: In the cross-sectional analyses, the study compared different
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patient outcomes based on the level of EHR utilization in hospitals. The reported outcomes
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included mortality rates, readmission rates, complications measured by PSIs, and length of
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stay. The statistical significance ogf the differences between these outcomes in hospitals with
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no EHR, partial EHR, and full EHR was evaluated using p-values.
Multiple Regression Analysis: The study conducted multiple regression analyses to
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assess the relationship between EHR utilization and patient outcomes while controlling for
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important patient and hospital characteristics. The reported statistics include odds ratios
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(OR) and p-values, which indicate the significance of the differences in patient outcomes
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between hospitals with full EHR, partial EHR, and no EHR.
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Difference-in-Differences Analysis: The difference-in-differences analyses aimed to
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estimate the effect of implementing an EHR system on patient outcomes. The reported
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statistics include risk ratios, 95% confidence intervals (CI), and p-values. These statistics
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indicate the magnitude of the effect and its statistical significance.
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Results
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The analyses confirmed the following 2.7% were treated in a hospital with no EHR,
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55.9% were treated in a hospital with partial EHR, and 41.4% were treated in a hospital
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with full EHR. In the cross-sectional analyses, surgical patients treated at hospitals with full
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EHR had higher mortality rates (1.6%) than patients treated at hospitals with partial EHR
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(1.4%) or hospitals with no EHR (1.6%) (P 1⁄4 0.0086). Surgical patients treated at
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hospitals with full EHR had higher rates of complications measured by PSIs (3.7%) than
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patients treated at hospitals with partial EHR (3.0%) or no EHR (3.2%) (P < 0.0001).
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Medical patients treated at hospitals with full EHR had a lower mortality rate (3.7%) than
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patients treated at hospitals with partial EHR (4.0%) or no EHR (4.4%) (P < 0.0001). The
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study found that surgical patients treated at hospitals with full EHR had higher mortality
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rates than those treated at hospitals with partial EHR (p = 0.0086). Surgical patients
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treated at hospitals with full EHR had higher readmission rates than those treated at
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hospitals with partial EHR (p = 0.0005). Surgical patients treated at hospitals with full
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EHR had higher rates of complications measured by PSIs compared to patients treated at
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hospitals with partial EHR (p < 0.0001). Differences in mortality rates among surgical
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patients treated at hospitals with full EHR versus no EHR were analyzed. This hypothesis
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was covered by the multiple regression analysis, where the researchers examined the
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difference in mortality rates between surgical patients treated at hospitals with full EHR
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versus no EHR while controlling for important patient and hospital characteristics.
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The multiple regression analysis found no statistically significant difference in mortality
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rates among surgical patients treated at hospitals with full EHR versus no EHR (OR 1.24, p
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= 0.1442). The difference in readmission rates among surgical patients treated at hospitals
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with full EHR versus no EHR was also analyzed. This hypothesis was covered by the
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multiple regression analysis, where the researchers assessed the difference in readmission
398
rates between surgical patients treated at hospitals with full EHR versus no EHR while
399
controlling for relevant factors.
400
The multiple regression analysis found no statistically significant difference in
401
readmission rates among surgical patients treated at hospitals with full EHR versus no EHR
402
(OR 1.04, p = 0.5605). Finally, we analyzed the difference in complications measured by PSIs
403
among surgical patients treated at hospitals with full EHR versus no EHR. This hypothesis
404
was also covered by the multiple regression analysis, where the researchers analyzed the
405
difference in complications measured by PSIs between surgical patients treated at hospitals
406
with full EHR versus no EHR while considering other patient and hospital characteristics.
407
The multiple regression analysis found a significant difference in rates of complications
408
measured by PSIs among surgical patients treated at hospitals with full EHR versus no EHR
409
(OR 1.22, p = 0.0452).
410
Data set 1
411
Table 1.1 Association between Patient Outcomes and EHR Implementation Status.
IMPACT OF EHR SYSTEMS
18
412
## # A tibble: 6 x 6
413
##
Group
Outcome Full.EHR.Vs..No.EHR.~1 P.Value Partial.EHR.Vs..No.E~2 P.Value
414
##
415
## 1 Medical
Died
0.965 (0.88, 1.06)
0.4609
1.004(0.91, 1.10)
0.9375
416
## 2 Medical
Revisit 0.970 (0.92, 1.03)
0.2834
0.995 (0.94, 1.05)
0.8697
417
## 3 Medical
PSI
1.057 (0.85, 1.31)
0.6157
1.132 (0.92, 1.40)
0.2516
418
## 4 Surgical Died
1.236 (0.93, 1.64)
0.1442
1.236 (0.93, 1.64)
0.1788
419
## 5 Surgical Revisit 1.037 (0.92, 1.17)
0.5605
1.041 (0.92, 1.18)
0.5158
420
## 6 Surgical PSI
0.0452
1.116 (0.92, 1.36)
0.2679
421
## # i abbreviated names: 1: Full.EHR.Vs..No.EHR.OR..CI.y,
422
## #
1.222 (1.00, 1.49)
2: Partial.EHR.Vs..No.EHR.OR..CI.y
423
Dataset 2
424
Table 2.1 Number of patients with percentages of No EHR, Partial EHR, and Full
425
EHR.
426
## # A tibble: 7 x 5
427
##
Procedure Patients no.EHR Partial.EHR Full.EHR
428
##
429
## 1 CABG
36,493
3.70%
53.00%
43.29%
430
## 2 AAA
1,779
1.69%
55.54%
42.78%
431
## 3 EVAR
6,930
3.22%
52.01%
44.78%
432
## 4 LOBE
8,708
1.53%
50.53%
47.94%
433
## 5 COLO
17,148
3.00%
54.27%
42.73%
434
## 6 HIP
66,104
2.19%
59.01%
38.80%
435
## 7 Total
1,37,162 2.70%
55.88%
41.42%
436
Table 2.2 Regression Analysis for Partial and Full Use of EHR
IMPACT OF EHR SYSTEMS
19
437
## # A tibble: 15 x 6
438
##
Regression.Statistics X
X
X
X
439
##
440
##
1 "Multiple R"
"0.9560833"
""
""
""
""
441
##
2 "R Square"
"0.914095277"
""
""
""
""
442
##
3 "Adjusted R Square"
"0.892619096"
""
""
""
""
443
##
4 "Standard Error"
"0.009778576"
""
""
""
""
444
##
5 "Observations"
"6"
""
""
""
""
445
##
6 ""
""
""
""
""
""
446
##
7 "ANOVA"
""
""
""
""
""
447
##
8 ""
"df"
"SS"
"MS"
"F"
"Sig~
448
##
9 "Regression"
"1"
"0.004069918"
"0.0040699~ "42.~ "0.0~
449
## 10 "Residual"
"4"
"0.000382482"
"9.56205E-~ ""
""
450
## 11 "Total"
"5"
"0.0044524"
""
""
""
451
## 12 ""
""
""
""
""
""
452
## 13 ""
"Coefficients" "Standard Error" "t Stat"
453
## 14 "Intercept"
"0.955429756"
"0.063709906"
"14.996565~ "0.0~ "0.7~
454
## 15 "X Variable 1"
"-0.956122671" "0.146553572"
"-6.524048~ "0.0~ "-1.~
455
"P-v~ "Low~
The regression analysis of these two variables indicates a positive relationship, as
456
shown by the high multiple R and R square at 0.956 and 0.914, respectively. Besides, the
457
regression indicates a low standard error of 0.00978, thus the high positive relationship
458
between the two variables.
459
We tested the following:
460
Table 2.3 Correlation Matrix
461
## # A tibble: 3 x 3
NA.
IMPACT OF EHR SYSTEMS
20
462
##
Correlation
NA.
NA.
463
##
464
## 1 ""
465
## 2 "Partial EHR" 1
""
466
## 3 "Full EHR"
"1"
Partial EHR "Full EHR"
-0.9560833
Correlation measures the relationship between two data variables, whereby a -1 or 1
467
468
indicates a strong relationship between the two variables. Therefore, with the -0.956, the two
469
variables indicate close relationships, whereby there is a close connection between the partial
470
and full EHR implementation.
Table 2.4 Mortality Rates with No EHR, Partial EHR, and Full EHR
471
472
## # A tibble: 5 x 4
473
##
Mortality.Rates NA.
NA.
NA.
474
##
475
## 1 Group
no EHR Partial EHR Full EHR
476
## 2 MI
6.78
5.51
5.04
477
## 3 CHF
3.6
3.27
2.88
478
## 4 Pneumonia
3.81
3.50
3.55
479
## 5 Total
4.36
3.95
3.71
480
The mortality rate of patients under the three healthcare systems, including no, partial
481
and full EHR implementation, are assessed. The data was adapted from a published study
482
to evaluate EHR usage and quality in healthcare (Yanamadala et al., 2016). The table above
483
indicates the data acquired to showcase the mortality rate in different regions and hospitals.
484
In the above table we can see how the mortality rate is different for patient with Myocardial
485
Infarction, Congestive Heart Failure, and Pneumonia under No EHR, Partial EHR, and Full
486
EHR.
IMPACT OF EHR SYSTEMS
487
21
We used R (Version 4.2.2; R Core Team, 2022) and the R-packages docxtractr (Version
488
0.6.5; Rudis & Muir, 2020), DT (Version 0.28; Xie, Cheng, & Tan, 2023), formattable
489
(Version 0.2.1; Ren & Russell, 2021), gt (Version 0.9.0; Iannone et al., 2023), huxtable
490
(Version 5.5.2; Hugh-Jones, 2022), kableExtra (Version 1.3.4; Zhu, 2021), knitr (Version 1.42;
491
Xie, 2015), officer (Version 0.6.2; Gohel, 2023), papaja (Version 0.1.1.9001; Aust & Barth,
492
2022), and tinylabels (Version 0.2.3; Barth, 2022) for all our analyses.
IMPACT OF EHR SYSTEMS
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555
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abstract: |
This proposal outlines an investigation into the ramifications of implementing Electronic
Health Record (EHR) systems regarding delivering high quality healthcare; specifically
analyzing effects pertaining to patient safety standards, documentation accuracy
completeness and timeliness as well as overall efficiency measures. Our methodology
entails collecting information through secondary data sources such as medical and
administrative databases alongside surveys conducted regarding staff experiences with post
implementation periods – including any potential benefits or drawbacks experienced. Our
approach includes statistical techniques such as regression analysis or hypothesis testing
which will offer insights into possible relationships between EHR implementation and
healthcare outcomes. This study has the potential to add significant value to current
knowledge bases within health informatics and help in supporting decision makers
worldwide towards EHR adoption with informed understandings of effects on various
aspects of care delivery. This research adds to the body of healthcare informatics literature
by drawing on publicly accessible secondary data to examine the influence of EHR
systems on vital components of healthcare delivery.
keywords
: "Electronic Healthcare Record Systems, Healthcare Delivery, Patient
Safety, Healthcare Quality, Healthcare Efficiency."
wordcount
: "3571"
bibliography
: "r-references.bib"
floatsintext
: no
linenumbers
: yes
draft
: no
mask
: no
figurelist
: no
tablelist
: no
footnotelist
: no
classoption
output
---
: "man"
: papaja::apa6_pdf
```{r setup, include = FALSE}
library("papaja")
library(docxtractr)
r_refs("r-references.bib")
```
```{r analysis-preferences}
# Seed for random number generation
set.seed(42)
knitr::opts_chunk$set(cache.extra = knitr::rand_seed)
```
By offering digital platforms for securely storing, managing, and distributing patient data,
the implementation of EHR systems has radically altered the healthcare context. Improved
documentation accuracy, augmented patient safety, and augmented operational efficiency
are some potential advantages of EHR systems (Lavin et al., 2015). As more healthcare
organizations adopt EHR systems, it is vital to scrutinize their influence on healthcare
quality, patient safety, and efficiency. Gaining insights into these effects is crucial for
smart decision-making, facilitating superior healthcare outcomes, and optimizing patient
care. The shift from conventional paper-based medical records to electronic health record
systems has been motivated by the acknowledgement that EHR systems possess the
capability to transform the delivery and management of healthcare (Aguirre et al., 2019).
The process of digitizing patient records enables healthcare practitioners to promptly
retrieve extensive patient data, thereby enhancing the precision of diagnoses, treatment
choices, and care management (Wheatley, 2013). Electronic Health Record systems offer
advantages that go beyond individual patient visits. They facilitate the smooth exchange of
data across various healthcare environments, guaranteeing consistency of care and
minimizing the likelihood of medical errors that may result from inadequate or disjointed
information. The primary rationale behind the analysis of the influence of Electronic
Health Record systems on healthcare quality is to guarantee precise and comprehensive
documentation. The absence of precise or comprehensive documentation can result in
grave implications for the provision of healthcare to patients, such as medical inaccuracies,
postponed diagnoses, and jeopardized patient welfare. According to Evans (2016),
Electronic Health Record systems have the potential to improve documentation accuracy
by reducing errors resulting from illegible handwriting, lost or misplaced records, and
incomplete data. Examining how the adoption of EHRs has affected documentation
accuracy enables healthcare providers to better target their resources and develop strategies
for delivering better care. In addition, the promptness of documentation plays a crucial role
in guaranteeing effective and synchronized provision of care. The prompt and efficient
retrieval of patient data is a critical component for healthcare practitioners to make wellinformed judgments, administer suitable therapies, and effectively track patient
advancement (Adane, 2019). Electronic Health Record systems facilitate instantaneous
retrieval and updating of patient data, thereby enabling healthcare providers to access realtime information (Wheatley, 2013). The implementation of technology in healthcare not
only optimizes the speed and effectiveness of medical procedures, but also facilitates
expeditious correspondence between healthcare professionals, resulting in enhanced patient
results and diminished healthcare expenditures. The adoption of electronic health record
systems presents favorable prospects for augmenting patient safety and mitigating the
incidence of avoidable harm. Electronic health record systems offer a range of features,
including medication reconciliation, tools for decision-support, and clinical alerts, that can
assist medical professionals in making more educated and safer decisions (Lindén-Lahti et
al., 2022). Health care facilities may determine areas where electronic health record
systems have significantly improved patient safety by studying the impact of EHR
deployment on medication mistakes, negative outcomes, and hospital-acquired infections.
They may then create plans to tackle any unresolved problems or risks.
Influence Of Electronic Health Record Systems on Healthcare Workflow and Productivity
The effective adoption of Electronic Health Record systems within healthcare
establishments holds the capacity to optimize operational processes, elevate output, and
augment overall efficacy. Electronic Health Record systems provide a range of capabilities
and characteristics that mechanize and digitalize diverse administrative and clinical duties,
thereby diminishing the dependence on manual and paper-oriented procedures (Aguirre et
al., 2019). Comprehending the effects of electronic health record implementation on
healthcare processes and productivity is of paramount importance for healthcare entities
seeking to enhance their operational efficiency and provide superior patient care.
Optimizing Administrative Processes Appointment scheduling, invoicing, and the filing of
insurance claims may all be handled more efficiently with the help of an electronic health
record system. These systems offer centralized databases for the storage of patient data,
thereby negating the need for input of data manually and diminishing the probability of
errors (Ehrenstein et al., 2018). The automation of administrative tasks within healthcare
organizations has the potential to optimize workflows, enhance resource allocation, and
boost productivity. Electronic Health Record systems facilitate the process of appointment
scheduling by enabling healthcare providers to access and review available time slots,
reserve appointments, and dispatch automated notifications to patients. The
implementation of an automated appointment scheduling system alleviates the task of
manual schedule management and mitigates the potential for scheduling conflicts or
overlooked appointments (Howard et al., 2020). Furthermore, faster and accurate billing
procedures are made possible by the connection of EHR systems with billing and claims
processing activities. Billing forms automatically include patient information, saving time
and effort by eliminating the need for human data input. EHR systems also offer
capabilities for verifying insurance coverage and submitting claims, minimizing paperwork
and speeding up payment procedures. Optimization of Data Retrieval and Information
Sharing Processes EHR systems facilitate the expedient retrieval of data and the sharing of
information among medical professionals, resulting in increased productivity and work
efficiency. In the past, healthcare practitioners were dependent on tangible paper charts or
the transmission of documents via facsimile for the purpose of exchanging information
(Evans, 2016). Electronic health record systems make patient information easily available
at the point of treatment, doing away with the need for manual searches while also cutting
down on time wasted. By utilizing modern technology, medical practitioners are able to
swiftly retrieve comprehensive medical records, laboratory findings, and radiographic
assessments, facilitating prompt clinical decision-making and optimizing operational
productivity. Electronic health record systems provide extensive search functionalities,
enabling healthcare professionals to swiftly access particular data or monitor changes in
patient status throughout the course of treatment (Quinn et al., 2019). The prompt
availability of patient information empowers healthcare practitioners to make informed
judgments, devise therapeutic strategies, and deliver prompt interventions. In addition,
electronic health record systems facilitate the secure exchange of patient data among
various healthcare settings, thereby promoting efficient care coordination and mitigating
communication barriers. In the past, the exchange of patient records among healthcare
providers necessitated laborious procedures, such as transmitting physical records via
facsimile or postal mail (Ajami & Bagheri-Tadi, 2013). Electronic health records systems
facilitate the electronic interchange of patient data, making it accessible to authorized
healthcare professionals (Ajami & Bagheri-Tadi, 2013). The optimization of information
sharing facilitates effective communication, minimizes redundant testing, and amplifies
workflow efficiency in diverse healthcare environments, including medical centers,
pharmacies, and, hospitals. Workflow Integration and Clinical Decision Support Electronic
Health Record systems frequently integrate clinical decision support mechanisms that offer
healthcare providers real-time notifications, prompts, and evidence-based
recommendations (Lavin et al., 2015). The utilization of these tools facilitates the process
of clinical decision-making and increases the efficacy of workflow (Sutton et al., 2020).
Medication-related adverse events may be avoided, for instance, if electronic health record
systems are used to highlight the possibility of drug interactions, allergies, or dosing
mistakes. The EHR's built-in clinical decision support capabilities examine the patient's
medical history, including their prescriptions, allergies, and diseases, and then provide
warnings or suggestions based on these analyses. These cautions might be anything from
reminders about preventative screenings or vaccines to warnings about interactions
between medications (Sutton et al., 2020). By incorporating decision support tools into
electronic health record systems, medical professionals are able to make choices that are
better informed, hence lowering the risk of making errors and increasing the possibility that
patients will be safe. Electronic Health Record systems have the capability to enhance
workflow integration by establishing connections among different departments and
healthcare practitioners who are engaged in providing patient care. When a physician
requests laboratory tests using an EHR system, for example, the request is transmitted
electronically to the laboratory. This eliminates the requirement for manual order input and
reduces the amount of time it takes to complete the tests (Wheatley, 2013). The findings
are subsequently sent in a computerized way to the EHR system, where they are made
immediately available to the physician who placed the prescription. The integration of
various care teams results in a reduction of time and effort needed for coordination and
communication, leading to streamlined workflows and increased productivity (Wheatley,
2013). Workforce Productivity and Job Satisfaction Healthcare workers' levels of job
satisfaction and productivity may change with the introduction of electronic health record
systems. Medical professionals generally report more work satisfaction and less
administrative stress after mastering electronic health records, despite the fact that the
switch from paper-based systems sometimes need training and adjustment periods (Ajami,
2013). Electronic health record systems have the potential to streamline documentation
procedures through the provision of drop-down menus, templates, and voice recognition
functionalities, thereby facilitating accelerated and precise documentation. As opposed to
using handwritten or transcribed information, healthcare personnel may immediately enter
patient interactions into the EHR system, eliminating the risk of mistakes caused by
handwriting that is indecipherable or missing information (Evans, 2016). In addition,
electronic health record systems come with features like copy-and-paste and auto-fill, both
of which help to simplify paperwork even more and save time. In addition, electronic
health record systems allow for quicker data retrieval, since they do away with the need to
physically find documents or sift through piles of paper charts. The ability for medical
professionals to swiftly access patient information, go through patients' medical histories,
and evaluate test results enables them to make more effective clinical decisions (Manca,
2015). The provision of instantaneous data additionally facilitates enhanced
communication and collaboration amongst healthcare teams, thereby promoting
synchronized patient care. The aforementioned factors are conducive to enhanced job
satisfaction, as healthcare professionals are able to allocate more attention towards patient
care and minimize the amount of time dedicated to administrative duties. The alleviation of
paperwork and administrative duties enables healthcare practitioners to devote additional
time and focus to face-to-face patient engagements, improving their sense of fulfillment
and purpose (Barello et al., 2015). Improved satisfaction with work, in turn, may boost
employee productivity, resulting in higher-quality treatment for patients and improved
medical results. Influence of Electronic Health Record Systems on Patient Safety and
Quality of Care The adoption of Electronic Health Record systems within healthcare
institutions has resulted in notable progressions in patient safety and the general standard
of care. EHR systems include features and functions that help with timely and accurate
documentation, better drug control, fewer adverse events, and improved provider
communication (Evans, 2016). Comprehending the effects of EHR implementation on the
safety of patients and care quality is imperative for healthcare institutions seeking to
improve patient outcomes and establish a safe environment for care. Improved Medication
Administration EHR systems are essential for enhancing drug administration and
minimizing pharmaceutical errors, which may have detrimental effects on patient safety.
Healthcare practitioners may electronically prescribe pharmaceuticals using EHR systems,
eliminating the dependence on handwritten prescriptions, which are prone to mistakes
(Porterfield, et al., 2015). The incorporation of e-prescribing functionalities in electronic
health record systems is accompanied by inherent safety protocols, including drug-drug
interaction notifications, allergy advisories, and dosage suggestions, that facilitate the
informed decision-making of healthcare practitioners in the course of prescribing
medication. In addition, electronic health record systems enable precise reconciliation of
medication through the provision of a comprehensive overview of a patient's medication
history, encompassing present medications, previous prescription drugs, and drug allergies
(Gildon et al., 2019). This data assists healthcare professionals in verifying the suitability
of prescribed medications, preventing redundancy, and reducing the likelihood of negative
drug responses. EHR systems improve patient safety and the provision of high-quality care
by lowering medication mistakes and managing medications better. Reduced Adverse
Events and Infections Acquired in Hospitals The adoption of electronic health record
systems has demonstrated potential in mitigating the incidence of unfavorable incidents
and nosocomial infections, thereby enhancing patient safety. Electronic Health Record
systems facilitate healthcare providers' access to current patient data, such as laboratory
findings, diagnostic assessments, and vital signs, during the provision of care (Ehrenstein
et al., 2019). The swift availability of crucial patient information enables healthcare
practitioners to make well-informed decisions rapidly, resulting in the prompt detection
and intervention of declining patient conditions. Furthermore, EHR systems include
clinical decision support technologies that give healthcare practitioners with real-time
alerts and reminders. These alerts may let medical professionals know about possible
safety hazards including allergies, contraindications, or unexpected test results, allowing
for prompt attention and the right course of action (Lavin et al., 2015). The integration of
clinical decision support into electronic health record systems enables healthcare
organizations to enhance patient safety by reducing the number of adverse occurrences that
may have been avoided. EHR systems can enhance care coordination by improving
communication between healthcare providers. Healthcare providers have the ability to
electronically exchange patient data, such as treatment plans, notes on progress, and
summaries of discharge, to facilitate comprehensive and collaborative care delivery
involving all pertinent stakeholders (Lindén-Lahti et al., 2022). This all-encompassing and
well-coordinated approach to patient care decreases the risk of making mistakes, boosts
patient safety, and increases the quality of treatment as a whole. This research uses
electronic healthcare records datasets from Kaggle, the UCI Machine Learning Repository,
and the Harvard Dataverse databases to examine the effects of deploying Electronic Health
Record systems on the quality of healthcare, safety for patients, and efficiency. The EHR
datasets should contain an exhaustive compilation of electronic medical records from a
variety of healthcare facilities. It consists of the patient's demographic information as well
as their medical diagnoses, treatments, test findings, prescription records, and any other
pertinent information that has been documented inside EHR systems. This dataset is a
valuable source of data that may be used to investigate the links between the
implementation of EHR systems and the results of healthcare. Research Questions: Using
the datasets obtained, this study aims to examine the following questions:
RQ1. How does the implementation of EHR systems affect the overall quality of healthcare
services provided, including the accuracy, completeness, and timeliness of documentation?
RQ2. What are the specific improvements in patient safety observed after the adoption of
EHR systems, such as reductions in medication errors, adverse events, or hospital-acquired
infections?
RQ3. How does the implementation of EHR systems impact healthcare efficiency
measures, such as the time spent on documentation, patient wait times, or resource
utilization?
RQ4. What are the perceived benefits and drawbacks reported by healthcare providers
following the implementation of EHR systems, and how do they influence workflow,
productivity, and job satisfaction?
H1: There is a positive correlation between the implementation of Electronic Health
Record systems in healthcare organizations and healthcare quality.
H2: The implementation of EHR systems improves patient safety in healthcare settings.
H3: The adoption of EHR systems in healthcare delivery positively impacts efficiency by
streamlining administrative tasks, reducing paperwork, and eliminating duplicate data
entry.
# Methods
Through use of descriptive analysis, inferential statistics and regression modelling
methods, this research seeks an elaborate understanding concerning how implementing
Electronic Health Record impacts various aspects within healthcare delivery such as
quality, patient safety, and efficiency. Datasets derived from sources like Kaggle, Harvard
Dataverse, and UCI Machine Learning Repository provide essential details concerning
patient histories inclusive but not limited to medication records, laboratory results and
other relevant details.
Electronic health records (EHRs) were implemented to improve the quality of care and
patient outcomes. This study assessed the relationship between EHR adoption and patient
outcomes. We performed an observational study using State Inpatient Databases linked to
the American Hospital Association survey, 2011. This study aimed to determine whether
hospitals with fully implemented EHR systems had better patient care than hospitals with
partial or no implemented EHR systems after controlling for other important patient and
hospital characteristics. This study used cross-sectional analyses, comparing patient
outcomes (mortality rates, readmission rates, complications) among hospitals with no
EHR, partial EHR, and full HER. This data was used to test the hypothesis that. Hypothesis
2: The implementation of EHR systems improves patient safety in healthcare settings.
After that, I also needed to confirm the hypothesis that
Hypothesis 1: There is a positive correlation between the implementation of Electronic
Health Record systems in healthcare organizations and healthcare quality.
Hypothesis 3: The adoption of EHR systems in healthcare delivery positively impacts
efficiency by streamlining administrative tasks, reducing paperwork, and eliminating
duplicate data entry.
To evaluate EHR systems' usage and effectiveness in enhancing safety, quality, and
efficiency. In this case, there was no similar dataset. However, I found enough data from a
secondary online publication conducted in 2011 by Yanamadala et al., 2016.
We utilized discharge data from the 2011 State Inpatient databases (SID), Healthcare Cost
and Utilization Project (HCUP), and Agency for Healthcare Research and Quality
accessible on Kaggle, Harvard Dataverse, and UCI Machine Learning Repository. SID is
an all-capture state database that allows the linkage of Patients over time and contains
information on patient characteristics, primary and secondary diagnoses, and procedures
received. Patient safety indicators (PSI) are based on ICD-9-CM codes and Medicare
severity diagnosis-related groups (DRGs), with specific inclusion and exclusion criteria
determined by the Agency for Healthcare Research and Quality (AHRQ). Each PSI
includes a unique denominator, numerator, and set of risk adjustors.
## Data
The present investigation will acquire the requisite data from the designated data set from
the EHR, which is accessible on Kaggle, Harvard Dataverse, and UCI Machine Learning
Repository. The electronic healthcare records in this collection, which come from multiple
healthcare institutions, include in-depth data on demographics of patients, health
diagnoses, treatments, test findings, prescription records, and other pertinent factors. The
data set will include a large number of patient records, enabling for a thorough examination
of the effect of EHR deployment on healthcare quality, safety for patients, and efficiency.
We performed an observational study using State Inpatient Databases linked to the
American Hospital Association survey, 2011. This study aimed to determine whether
hospitals with fully implemented EHR systems had better patient care than hospitals with
partial or no implemented EHR systems after controlling for other important patient and
hospital characteristics.
This study assessed the relationship between EHR adoption and patient outcomes. We
performed an observational study using State Inpatient Databases linked to the American
Hospital Association survey, 2011. This study aimed to determine whether hospitals with
fully implemented EHR systems had better patient care than hospitals with partial or no
implemented EHR systems after controlling for other important patient and hospital
characteristics. This study used cross-sectional analyses, comparing patient outcomes
(mortality rates, readmission rates, complications) among hospitals with no EHR, partial
EHR, and full EHR. The data cleaning process involved collecting, evaluating, formatting,
assessing inconsistencies and errors in the data set.
Data set 1 Description The data used in the study consisted of information on surgical and
medical patients treated at various hospitals. The study included a total of 137,162 surgical
patients and 311,605 medical patients. The hospitals were categorized based on their level
of electronic health record (EHR) utilization. The categories included hospitals with no
EHR, hospitals with partial EHR, and hospitals with full EHR. The percentages of patients
treated at each type of hospital were reported. Patient characteristics, such as demographics
and comorbidities, were also recorded. These characteristics were considered as
independent variables in the statistical analyses. The dependent variables in the study were
mortality, readmissions, and complications measured by Patient Safety Indicators (PSIs).
These variables were used to assess the quality of care provided to the patients. The study
employed multiple regression analysis and difference-in-differences analyses to investigate
the relationship between EHR utilization and quality of care. The statistical analyses aimed
to determine if there were statistically significant differences in patient outcomes based on
the level of EHR implementation in the hospitals. The statistical analyses were performed
using software packages, specifically STATA version 13.0 and SAS version 9.3. The
specific statistical models and methods used were not explicitly mentioned in the excerpt
provided.
To evaluate EHR systems' usage and effectiveness in enhancing safety, quality, and
efficiency. In this case, there was no similar data set. However, I found enough data from a
secondary online publication conducted in 2011 by Yanamadala et al., 2016. Data set 2
Description The data evaluated had a total number of patients assessed amounting to
137,162, whereby those served under the partial EHR system accounted for 55.88%
(Yanamadala et al., 2016). Besides, the number of patients served under full EHR was
relatively lower than in the case of partial EHR applications. Besides, the no EHR case had
the least number of patients accounting for 2.7% of the total patients. We assessed the
patients on basis of who had undergone the procedures like Coronary Artery Bypass
Graft(CABG), Abdominal Aortic Aneurysm Repair(AAA), Endovascular Aneurysm
Repair(EVAR), Lateral Orbitotomy with Bony decompression(LOBE),
Colonoscopy(COLO), and Total Hip Replacement(HIP) procedures under No EHR, Partial
EHR, and Full EHR.
## Predictors
We utilized discharge data from the 2011 State Inpatient databases (SID), Healthcare Cost
and Utilization Project (HCUP), and Agency for Healthcare Research and Quality
accessible on Kaggle, Harvard Dataverse, and UCI Machine Learning Repository. SID is
an all-capture state database that allows the linkage of Patients over time and contains
information on patient characteristics, primary and secondary diagnoses, and procedures
received. Patient safety indicators (PSI) are based on ICD-9-CM codes and Medicare
severity diagnosis-related groups (DRGs), with specific inclusion and exclusion criteria
determined by the Agency for Healthcare Research and Quality (AHRQ). Each PSI
includes a unique denominator, numerator, and set of risk adjustors.
## Outcome
The quality of medical treatment, the safety of patients, and how efficiently resources are
used are going to be the key focus measures of this study. The evaluation of these
outcomes will be conducted through the utilization of diverse indicators, including but not
limited to preciseness, comprehensiveness, and documentation timeliness, to gauge the
quality of care delivered. Medication mistakes, adverse events, and hospital-acquired
infections will be examined to determine patient safety improvements. The efficiency
metrics will prioritize factors such as the duration of documentation, the duration of patient
wait times, and the utilization of resources. Furthermore, secondary outcome measures like
the perceived advantages and negative aspects indicated by healthcare personnel, such as
productivity, workflow, and job satisfaction, will be taken into consideration.
## Data Analytic Plan
For the purpose of this investigation, we will do an analysis of the data that combines
descriptive analysis, and regression modeling. The utilization of descriptive statistics is
intended to provide a summary of the dataset's features, encompassing the variables
distribution and measures of summary. Regression modeling, which accounts for possible
confounding variables, will be used to evaluate the relationships between the determinant
variables (EHR implementation factors) and the variables that determine the outcome. In
order to guarantee the reliability of the outcomes, proper statistical methods will be used,
taking into account the characteristics of the variables and the inquiries posed by the study.
The potential for EHR implementation's effects to vary among populations or
environments may be investigated using sensitivity and subgroup studies. A determination
will be made on the statistical significance of the relationships as well as the size of the
effects, and any assumptions or limitations regarding the research will be evaluated and
explained.
The dataset analysis can be summarized as follows:
Descriptive Statistics: The study used univariate regression analysis to develop descriptive
statistics.
Cross-Sectional Analyses: In the cross-sectional analyses, the study compared different
patient outcomes based on the level of EHR utilization in hospitals. The reported outcomes
included mortality rates, readmission rates, complications measured by PSIs, and length of
stay. The statistical significance ogf the differences between these outcomes in hospitals
with no EHR, partial EHR, and full EHR was evaluated using p-values.
Multiple Regression Analysis: The study conducted multiple regression analyses to assess
the relationship between EHR utilization and patient outcomes while controlling for
important patient and hospital characteristics. The reported statistics include odds ratios
(OR) and p-values, which indicate the significance of the differences in patient outcomes
between hospitals with full EHR, partial EHR, and no EHR.
Difference-in-Differences Analysis: The difference-in-differences analyses aimed to
estimate the effect of implementing an EHR system on patient outcomes. The reported
statistics include risk ratios, 95% confidence intervals (CI), and p-values. These statistics
indicate the magnitude of the effect and its statistical significance.
## Results
The analyses confirmed the following 2.7% were treated in a hospital with no EHR, 55.9%
were treated in a hospital with partial EHR, and 41.4% were treated in a hospital with full
EHR. In the cross-sectional analyses, surgical patients treated at hospitals with full EHR
had higher mortality rates (1.6%) than patients treated at hospitals with partial EHR (1.4%)
or hospitals with no EHR (1.6%) (P 1⁄4 0.0086). Surgical patients treated at hospitals with
full EHR had higher rates of complications measured by PSIs (3.7%) than patients treated
at hospitals with partial EHR (3.0%) or no EHR (3.2%) (P \< 0.0001). Medical patients
treated at hospitals with full EHR had a lower mortality rate (3.7%) than patients treated at
hospitals with partial EHR (4.0%) or no EHR (4.4%) (P \< 0.0001). The study found that
surgical patients treated at hospitals with full EHR had higher mortality rates than those
treated at hospitals with partial EHR (p = 0.0086). Surgical patients treated at hospitals
with full EHR had higher readmission rates than those treated at hospitals with partial EHR
(p = 0.0005). Surgical patients treated at hospitals with full EHR had higher rates of
complications measured by PSIs compared to patients treated at hospitals with partial EHR
(p \< 0.0001). Differences in mortality rates among surgical patients treated at hospitals
with full EHR versus no EHR were analyzed. This hypothesis was covered by the multiple
regression analysis, where the researchers examined the difference in mortality rates
between surgical patients treated at hospitals with full EHR versus no EHR while
controlling for important patient and hospital characteristics.
The multiple regression analysis found no statistically significant difference in mortality
rates among surgical patients treated at hospitals with full EHR versus no EHR (OR 1.24, p
= 0.1442).
The difference in readmission rates among surgical patients treated at hospitals with full
EHR versus no EHR was also analyzed. This hypothesis was covered by the multiple
regression analysis, where the researchers assessed the difference in readmission rates
between surgical patients treated at hospitals with full EHR versus no EHR while
controlling for relevant factors.
The multiple regression analysis found no statistically significant difference in readmission
rates among surgical patients treated at hospitals with full EHR versus no EHR (OR 1.04, p
= 0.5605).
Finally, we analyzed the difference in complications measured by PSIs among surgical
patients treated at hospitals with full EHR versus no EHR. This hypothesis was also
covered by the multiple regression analysis, where the researchers analyzed the difference
in complications measured by PSIs between surgical patients treated at hospitals with full
EHR versus no EHR while considering other patient and hospital characteristics.
The multiple regression analysis found a significant difference in rates of complications
measured by PSIs among surgical patients treated at hospitals with full EHR versus no
EHR (OR 1.22, p = 0.0452).
CABG: Coronary Artery Bypass Graft
AAA: Abdominal Aortic Aneurysm Repair
EVAR: Endovascular Aneurysm Repair
LOBE: Lateral Orbitotomy with Bony Decompresion
COLO
HIP
Total
Data Visualization
Analyzing the Impact of Implementing EHR Systems on Healthcare Quality,
Patient Safety, and Efficiency
Impact of EHR Systems
Data Visualization
Pranitha Pittala
PPittala@my.harrisburgu.edu
Harrisburg University of Science and Technology
Professor: Roozbeh Sadeghian, PhD
Data Visualization
Data set # 1
Table 1.1 Association between Patient Outcomes and EHR Implementation Status.
Group
Outcom
e
Died
Revisit
PSI
Died
Revisit
PSI
Medical
Medical
Medical
Surgical
Surgical
Surgical
Full EHR Vs. No EHR
OR (CI)y
P Value
Partial EHR Vs. No
EHR OR (CI)y
P Value
0.965 (0.88, 1.06)
0.970 (0.92, 1.03)
1.057 (0.85, 1.31)
1.236 (0.93, 1.64)
1.037 (0.92, 1.17)
1.222 (1.00, 1.49)
0.4609
0.2834
0.6157
0.1442
0.5605
0.0452
1.004(0.91, 1.10)
0.995 (0.94, 1.05)
1.132 (0.92, 1.40)
1.236 (0.93, 1.64)
1.041 (0.92, 1.18)
1.116 (0.92, 1.36)
0.9375
0.8697
0.2516
0.1788
0.5158
0.2679
Full EHR Vs. No EHR - P
Values
0.6157
0.4609
Figure 1.1
Figure 1.2
Partial EHR Vs. No EHR - P
Value
0.2516
0.2834
0.9375
0.8697
Medical Died 0.965 (0.88, 1.06)
Medical Revisit 0.970 (0.92,
1.03)
Medical PSI 1.057 (0.85, 1.31)
Medical Died 1.004(0.91, 1.10)
Medical Revisit 0.995 (0.94, 1.05)
Medical PSI 1.132 (0.92, 1.40)
Data Visualization
Figure 1.3
Figure 1.4
Full EHR Vs. No EHR - P Values
Partial EHR Vs. No EHR - P
Value
0.0452
0.1442
0.2679
0.5605
0.1788
0.5158
Surgical Died 1.236 (0.93, 1.64)
Surgical Died 1.236 (0.93, 1.64)
Surgical Revisit 1.037 (0.92, 1.17)
Surgical Revisit 1.041 (0.92, 1.18)
Surgical PSI 1.222 (1.00, 1.49)
Surgical PSI 1.116 (0.92, 1.36)
Data set # 2
Table 2.1 Number of patients with percentages of No EHR, Partial EHR, and Full EHR.
Procedure
CABG
AAA
EVAR
LOBE
COLO
HIP
Total
Patients
36,493
1,779
6,930
8,708
17,148
66,104
1,37,162
no EHR
3.70%
1.69%
3.22%
1.53%
3.00%
2.19%
2.70%
Partial EHR
53.00%
55.54%
52.01%
50.53%
54.27%
59.01%
55.88%
Full EHR
43.29%
42.78%
44.78%
47.94%
42.73%
38.80%
41.42%
Data Visualization
Table 2.2 Regression Analysis for Partial and Full Use of EHR
Regression Statistics
Multiple R
0.9560833
R Square
0.914095277
Adjusted R
Square
0.892619096
Standard Error 0.009778576
Observations
6
ANOVA
df
Regression
Residual
Total
Intercept
X Variable 1
1
4
5
SS
0.004069918
0.000382482
0.0044524
Coefficients Standard Error
0.955429756
0.063709906
0.956122671
0.146553572
MS
F
Significance F
0.004069918 42.56321389
0.002850664
9.56205E-05
t Stat
P-value
14.9965652 0.000115191
Lower 95%
0.7785427
-6.524048888 0.002850664
-1.363020619
Data Visualization
Table 2.3 Correlation Matrix
Correlation
Partial EHR
Full EHR
Partial EHR
1
-0.9560833
Full EHR
1
Table 2.4 Mortality Rates with No EHR, Partial EHR, and Full EHR
Group
MI
CHF
Pneumonia
Total
no EHR
6.78
3.6
3.81
4.36
Mortality Rates
Partial EHR
5.51
3.27
3.50
3.95
Full EHR
5.04
2.88
3.55
3.71
Data Visualization
Figure 2.1 Percentages of EHR utilization
EHR Utilization
HIP
COLO
LOBE
EVAR
AAA
CABG
0.00%
38.80%
59.01%
2.19%
42.73%
54.27%
3.00%
47.94%
50.53%
1.53%
44.78%
3.22%
42.78%
1.69%
43.29%
3.70%
10.00%
20.00%
Full EHR
30.00%
Partial EHR
40.00%
50.00%
52.01%
55.54%
53.00%
60.00%
no EHR
Figure 2.2 Mortality Rates comparison with No EHR, Partial EHR, and Full EHR
Utilization for MI, CHF, and Pneumonia.
Data Visualization
Mortality Rates
8
7
6.78
6
5
5.51
5.04
4
3.81
3.6
3
3.27
3.5
3.55
2.88
2
1
0
MI
CHF
Mortality Rates no EHR
Pneumonia
Mortality Rates Partial EHR
Mortality Rates Full EHR
Figure 2.3 Averages of Mortality Rates with No EHR, Partial EHR, and Full EHR.
Averages of Mortality Rates
Full HER, 3.71, 31%
No HER, 4.36, 36%
Partial EHR, 3.95, 33%
No HER
Partial EHR
Full HER
Data Visualization
"Analyzing the impact of implementing EHR systems on healthcare quality,
patient safety, and efficiency."
Research Questions:
• How does the implementation of EHR systems affect the overall quality
of healthcare services provided, including accuracy, completeness, and
timeliness of documentation?
• What are the specific patient safety improvements observed after the
adoption of EHR systems, such as reductions in medication errors,
adverse events, or hospital-acquired infections?
• How does the implementation of EHR systems impact healthcare
efficiency measures, such as time spent on documentation, patient wait
times, or resource utilization?
• What are the perceived benefits and drawbacks reported by healthcare
providers following the implementation of EHR systems, and how do
they affect workflow, productivity, and job satisfaction?
• Hypothesis 1: There is a positive correlation between the implementation
of Electronic Health Record systems in healthcare organizations and
healthcare quality.
• Hypothesis 2: The implementation of EHR systems improves patient
safety in healthcare settings.
• Hypothesis 3: The adoption of EHR systems in healthcare delivery
positively impacts efficiency by streamlining administrative tasks,
reducing paperwork, and eliminating duplicate data entry.
• Hypothesis 4: The successful implementation of EHR systems is
influenced by several factors, including organizational readiness,
adequate training and support for healthcare professionals, effective
change management strategies, and interoperability between different
EHR systems.
Data Description:
Update your Methods section (particularly the Data subsection).
◦Detail any processing steps used for the data
◦Cleaning steps (e.g. cleaning text data, transforming variables) should go in Data
Analytic Plan subsection
◦Include summary statistics and concrete details about the data
Data Visualization:
Construct at least one table or figure for your project and list the tables and figures you plan
to include in your final paper. Your list may change but consider now what parts of your
project would benefit from a visualization. You can either insert these in your paper and
submit that or use a Word document to just submit your visualization and list.
Methods Section Revision
This assignment is part of an unpublished module and is not available yet.
Results Section Draft
This assignment is part of an unpublished module and is not available yet.
Discussion Section Draft
This assignment is part of an unpublished module and is not available yet.
Abstract Draft
This assignment is part of an unpublished module and is not available yet.
Final Project
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