Data-Driven Decision Making in Healthcare Journal Insights

Making Decisions Based on Data-Journal Shanta McGraw Southern New Hampshire University HIM-550-Q2400: Data Management & Data Quality 23TW2 Jennifer Horner 1/21/24

Making Decisions Based on Data-Journal "It is crucial for the healthcare sector to assess trends and create care systems that are efficient, effective, and reasonably priced for both patients and providers as healthcare costs rise and new consumer needs arise" (Nicholson and Penney, 2018). A trend in the Happy Health Hospital data collection is the use of artificial intelligence tools called healthcare bots to pull information from other tabs and add it to the data set for analysis. This may be ascertained by looking at the source of data column in the data set tabs labeled assessments, medications, lab system, and demographic data. This trend enables EHRs to analyze structured and unstructured data from a variety of sources, improving the accuracy of patient diagnosis, establishing a link between treatments and outcomes, and identifying patients who may be at risk of disease or readmission (McDonald, 2017). With the use of this kind of AI, data may be gathered for analysis, pattern recognition, trend and mistake detection, and data set examination. A re-occurring feature in the data gathering process at Happy Health Hospital is the irregular/random patterns of information. When an organization repeatedly violates guidelines, information patterns become irregular. If the policy was followed, these informational patterns would be more understandable and consistent in the data gathering. Under the assessment tab, this may be observed in the columns labeled #days before clinician recorded vital signs and #days before clinician recorded risk assessment. The values in the "# days before the clinician documented vital signs" column span more than two weeks, from less than 0.93 days to 14.68 days. The values in the "# days before the clinician documented the risk" column, on the other hand, span from 0.01 to 14.89 days, or two weeks. Assumptions

It is an assumption to accept information as true in the absence of formal facts. I would guess that after looking at the data set that Happy Health Hospital collected, the whole policy will need to be changed in order to ensure that the business is reducing the rates of falls because of the many regions that were not covered. Since non-adherence to the policy is the major reason for it, I would also expect that keeping it in place will lead to an increase in falls. A few of the policy components that are connected to this root cause are as follows: (1) all patient medications must be assessed for appropriateness by a nurse or doctor within 24 hours of admission; (2) all patient vital signs must be taken and recorded within 24 hours of admission; and (3) fall assessments and documentation for inpatient, emergency, and surgical cases must be completed within three days.

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References McDonald, Carol. (2017). Five big data trends in healthcare. Retrieved from https://www.itproportal.com/features/five-big-data-trends-in-healthcare/ Nicholson, Ruby. & Penney, David. (2018). Quality Data Critical to Healthcare Decision- Making. Retrieved from http://bok.ahima.org/doc?oid=106428#.YMomqFVKhhF

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