There has been lots of discussion about population health management and predictive analytics in the health field. Why? The majority of people who discuss these topics believe it’s a way to improve their health and cutting costs associated with doing it. Providing better care at lower costs is becoming necessary as payers are beginning to be able to pay for better outcomes as they move away from fee-for-service.

What exactly is population health? And what role does predictive analytics play in? Let me begin by explaining the concept of population health and show examples of the predictive analytics. In statistics, the term “population” refers to the complete set of items of interest for the study. For instance, it could refer to the temperature range of teenagers who have measles. It could be people living in rural areas who are prediabetic. Both are relevant in healthcare. It also applies to other research field. It may be the income of adults in a county or the ethnic communities living in the village.

Typically, population health management is the management of performance of people’s health through the lens of the entire group. For instance on a professional level, population health management would refer to providing a high-quality care for all patients within the practice. The majority of practices separate patients by diagnosis when using tools for population health management for instance, patients suffering from hypertension.

They typically concentrate on patients with high-cost for treatment so that more effective case management is provided to patients. Effective case management of a population typically leads to more satisfied patients and lower expenses. Visit:-

The perspective of population health of a health department in a county (as shown in the previous month’s newsletter) is a term used to describe all residents of a particular county. The majority of services provided by the health department aren’t provided to individuals. Instead, the health of residents of a county is improved by managing the environment in which they live. For example, health departments record the incidence of flu within a particular county to notify hospitals and providers to be able to offer the level of care needed.

You should be able to determine that the people whose health is being monitored is contingent on the person who provides the service. Physician practices’ population is all patients who are part who are a part of their practice. The county’s health department, it includes all residents of the county. For the CDC it’s all citizens from the United States.

Once the population is identified and the data that needs needed to collect is identified. In a clinical setting, a data or quality team is likely to be the entity which decides what data needs to be taken in. After data has been collected, patterns in the care of patients can be identified. For instance, a practice might discover there is a majority patients identified as hypertensive have managed their conditions well. The quality team determines that more can be done to improve the outcomes for patients who do not have their blood pressure in check. Using the factors from the results it’s gathered the team employs a statistical method known as predictive analytics to see if it is able to identify any issues that may be in common among people whose blood pressure is not well managed. For example, they could discover that patients do not have the funds to purchase their medication regularly and they face difficulties getting to the facility that offers their medical services. After identifying these problems the case manager at the clinic can work to overcome these challenges.

I’ll wrap up this discussion on population health and analytics with two examples of providers using the method correctly. In August 2013 , the Medical Group Management Association presented a webinar that featured the speaker Benjamin Cox, the director of Finance and Planning for Integrated Primary Care Organization at Oregon Health Sciences University, an organization that includes 10 primary care clinics and 61 physicians as well as Dr. Scott Fields, the Vice Chair of Family Medicine at the same company. The webinar’s title was “Improving Your Practice with Meaningful Clinical Data”. Two of the goals of the webinar were to establish the skills and capabilities of the Quality Data Team, including whom the team members were as well as describing the process of building a set of quality indicators.

Clinics already had many kinds of information to provide reports to different groups. For instance they were submitting data for “meaningful use” and to commercial payers as well as employees groups. They decided to collect these data points and organize it into scorecards which would serve individual physicians as well as practice managers at each clinic. Some of the information obtained was data on patient satisfaction as well as hospital readmissions data and obesity data. Scorecards designed for doctors were made to satisfy the requirements and requests of the physician as a whole as well as for the entire practice. For instance, a physician could ask to have his own scorecard which identified patients whose indicators for diabetes indicated they were in the middle of the limits that control his diabetes. A physician who is aware of this could spend more time improving the health of the patient.

Scorecards for the clinic showed how well the physicians at the facility were managing patients suffering from chronic conditions in the whole. By using predictive analytics, the staff of the clinic could identify what processes and procedures contributed to improving the health of the patients. Making sure that case management is more active could have been proven to be effective in patients with multiple chronic conditions.

He. Cox and Dr. Fields added that the staff members who were part of the data quality team were adept at understanding access, structuring data into useful ways, and in presenting data to clinicians effectively and collecting data from a variety of sources. The primary goals of the data team were to keep in balance the competing priorities of providing quality care as well as ensuring that operations ran smoothly and patient satisfaction was high.


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