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Population Level Health Management

There has been lots of discussions about population health management coupled with predictive analytics recently within the health care industry. Why? Most who are discussing the subject consider it as a way of improving their health while cutting costs associated with doing the same. Better care at lower cost is becoming more important as health insurance companies are starting to pay for quality outcomes, as they shift out of fee for service.

What exactly is population health? And what role does predictive analytics play in? Let me begin by defining the concept of population health and show examples of the predictive analytics. In statistics, the word “population” refers to the whole set of objects of interest to the study. For example, it could be the temperature range of adolescents who are measles-infected. It could be the individuals living in rural areas that are diabetic. Both are relevant in the field of healthcare. This is also applicable to any other research field. It could be the amount of income of the adults living in a county or ethnic group living in the village. Visit:- https://populer.co.id/

Typically, population health management is the management of performance of people’s health by looking at the collective group. For instance at the clinic level, the term “population health management is efficiently taking care of all patients within the practice. A majority of practices divide patients by diagnosis when using population health management tools for example, those with hypertension. Practices typically focus on patients who have high expenses to treat in order to ensure more efficient case management can be provided to them. Effective case management of patients typically results in more satisfied patients and lower costs.

The population health perspective of a county health department (as illustrated in last month’s newsletter) applies to all people living in a county. The majority of services provided by an health department aren’t offered to individual. The health of the residents of the county is enhanced by controlling the conditions that they live in. For instance, health departments track the frequency of flu cases in the county to alert providers and hospitals so that they can be prepared to provide the levels of medical care required.

It is important to see that the population whose health care is managed depends on who provides the service. The population of physician practices is all patients within the clinic. for county departments of health it is all residents living in a county. For the CDC it’s all residents in the United States.

Once the demographic is established after which the data needed to collect is determined. In a setting that is clinical, the quality or data team is likely to be the entity that determines what data should be taken in. After data has been collected, trends in care are able to be observed. For example, a clinic might find most of patients identified as having hypertension are managing their condition well. The quality team decides that more needs to be done to improve the outcomes for patients who do not maintain their blood pressure under control. Based on the variables from the data that it has collected the team applies a statistical approach called predictive analytics to see if there are any elements that could be common among people who are not effectively controlled. In particular, they may find that these patients lack funds to purchase their medications regularly and they struggle to get to the clinic which provides their care service. After identifying these problems the case manager at the clinic can help get over these hurdles.

I will finish this overview of the concept of health monitoring for populations and prescriptive analytics with two examples of providers using this approach in a correct manner. In August of 2013, the Medical Group Management Association presented an online webinar with the presenters Benjamin Cox, the director of Finance and Planning for Integrated Primary Care Organization at Oregon Health Sciences University, which has 10 primary health clinics and 61 doctors and Dr. Scott Fields, the Vice Chair of Family Medicine at the same organization. The title of the webinar was “Improving Your Practice with Meaningful Clinical Data”. The two goals of the event were to establish the skills of their Quality Data Team, including the names of their members and to describe the process to develop a set quality indicators.

The clinics already were collecting numerous types of information to share with various groups. For instance, they were reporting data for “meaningful use” and to commercial payers and employee groups. They decided to gather all of this information and compile it into scorecards which would be useful to individual physicians as well as practice managers at every clinic. Some of the data obtained was data on patient satisfaction along with hospital readmission information and data on obesity. Scorecards for doctors were created to satisfy the requirements and needs of the individual doctors and for the entire practice. For instance, a physician could request his own scorecard that identified specific patients with diabetes indicators that indicated they were in the middle of the limits that control his diabetes. Knowing this, a physician could devote more time to improving the health of the patient.

Scorecards of the clinic revealed how well the physicians on the premises dealt with patients with chronic conditions in the whole. By using predictive analytics, the staff of the clinic were able to determine the processes and actions that helped improve the health of patients. Offering more active case management might have proven to be effective for those suffering from multiple chronic diseases.

Mr. Cox and Dr. Fields also stated that the quality data team members were skilled at understanding the data’s accessibility, structuring it in useful ways, and in presenting data to clinicians effectively and collecting data from a variety of sources. The principal goals of the team’s data were to balance the competing agendas of providing high-quality care as well as ensuring that processes were effective and the patient’s satisfaction was high.

 

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