MDC Data Mining for Patient Outcomes Discussion

Write 2 discussions replies:

1. Data mining is a trustful set of data science methods used in evidence-based medical information. This is commonly used by clinicians and scientists. Through data mining, clinicians can effectively obtain information from structured and unstructured fields in the EHR.

EHR can extract, clean, and analyze data about a patient that includes diagnoses, medications, and lab results. In other words, EHR data analytics mine patient can improve patients care.

Data mining can benefit healthcare insurers sense fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more cheap healthcare services. Lastly, by mining through EHR, hospitals are ensuring patients’ positive outcomes by accessing care more efficiently, reducing readmission rates, and having more inexpensive care (Sarwar, 2023).

2. Data Mining

Data mining (DM) is extracting large amounts of data from databases with valuable information regarding a specific pathology, treatment, or outcome. Essentially DM can be used to find trends or similarities in data concerning cases and patient outcomes. Data mining can be used to predict future events, outbreaks, and trends such as pandemics like COVID and future ones based on historical data.

Relation to EHR

DM relates to the electronic health records (EHR) by extracting millions of HER that may relate to specific data a user may be mining to help curate successful care plans with good outcomes. EHR data may help identify positive outcomes, labs, diagnostics, prescriptions, trends, and treatments. Due to the tedious and arduous task of combing through thousands of records, more recently, artificial intelligence (AI) has been used to facilitate data extraction at faster rates.

Improving Patient Outcomes

One way DM may improve patient outcomes is by saving time and curating care plans faster than ever. Data mining combined with AI has the potential to identify patterns in patient data that could lead to an improved diagnosis and treatment of disease and care plan in a matter of minutes that yields the highest success rate. Moreover, data mining could help warn and identify patients at a higher risk for certain diseases and hazards and help design focused preventative plans.

Clinical Reasoning Importance

The process of arriving at a conclusion regarding patient treatment based on clinical evidence and knowledge is called clinical reasoning. DM can be filtered to only use the most compelling and recent data based on “evidence-based practice.” This is important because we want to DM only evidence-based data, as some data may yield outliers or may not be evidence-based and merely opinionated. Another useful importance that is attained with DM has the capability to increase sample sizes based on specified criteria through the EHR of all patients that have had said case and see percentages of patient outcomes along with their treatments and continuum of care.

Jointly, DM, AI, and clinical reasoning have the potential to revolutionize the health care system, patient care plan execution, and recovery time, as well as increase success in patient outcomes based on failed attempts and historically successful treatments.