Belhaven University Discussion

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Running head: IMPACT OF EHR SYSTEMS
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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
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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). 381 Medical patients treated at hospitals with full EHR had a lower mortality rate (3.7%) than 382 patients treated at hospitals with partial EHR (4.0%) or no EHR (4.4%) (P < 0.0001). The 383 study found that surgical patients treated at hospitals with full EHR had higher mortality 384 rates than those treated at hospitals with partial EHR (p = 0.0086). Surgical patients 385 treated at hospitals with full EHR had higher readmission rates than those treated at 386 hospitals with partial EHR (p = 0.0005). Surgical patients treated at hospitals with full 387 EHR had higher rates of complications measured by PSIs compared to patients treated at IMPACT OF EHR SYSTEMS 17 388 hospitals with partial EHR (p < 0.0001). Differences in mortality rates among surgical 389 patients treated at hospitals with full EHR versus no EHR were analyzed. This hypothesis 390 was covered by the multiple regression analysis, where the researchers examined the 391 difference in mortality rates between surgical patients treated at hospitals with full EHR 392 versus no EHR while controlling for important patient and hospital characteristics. 393 The multiple regression analysis found no statistically significant difference in mortality 394 rates among surgical patients treated at hospitals with full EHR versus no EHR (OR 1.24, p 395 = 0.1442). The difference in readmission rates among surgical patients treated at hospitals 396 with full EHR versus no EHR was also analyzed. This hypothesis was covered by the 397 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. 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