There has been a lot of discussions about population health management and predictive analytics within the health care industry. Why? Most who are discussing these topics consider it as a way of improving their health while reducing the costs of doing it. The ability to provide better care with lower costs is becoming necessary as health insurance companies are starting to be able to pay for better outcomes, as they shift out of fee for service.
What is population health and how can predictive analytics be incorporated in? Let me begin by explaining population health and illustrate predictive analytics. In statistics, the term “population” refers to the complete set of things that are of interest to the research. For instance, it could be the temperature of adolescents suffering from measles. This could include people living in rural areas that are diabetic. Both of them are relevant in healthcare. The concept of population also applies to other research field. It may be the income of the adults living in a county or the ethnic groups living in villages.
Typically, population health management is the process of managing health outcomes of individuals in the context of the group. For instance on a clinical practice level, population health management refers to providing a high-quality care for all patients of the practice. Most practices segregate the patients based on their diagnosis when they use population health management tools, such as patients with hypertension.
The majority of practices focus on patients with high-cost for care to ensure that more efficient case management can be provided to patients. Effective case management of patients typically results in more satisfied patients and lower expenses. Visit:- https://populer.co.id/
The perspective of population health of the county health department (as illustrated in last month’s newsletter) is a term used to describe all people living in a county. Most services of the health department aren’t offered to individual. The health of the residents of the county is enhanced by controlling the environment in which they live. For instance, health departments monitor the flu incidence in the county to notify hospitals and providers so that they are ready to provide the medical care required.
You should be able to see that the population whose health care is provided depends on the person who provides the service. The patients of physician practices are all the patients of the practice. for county departments of health, it is all residents of a county. For the CDC it’s all the residents from the United States.
Once the group is identified, the data needed to collect is determined. In a clinical setting, a quality or data team is most likely the body that determines what data should be taken in. After data has been collected, changes in treatment are able to be observed. For example, a doctor might discover it is the case that the vast majority patients who are identified as hypertensive manage their hypertension well. The quality team decides that more can be done to improve the outcome for patients who do not control 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 is able to identify any issues that may be in common for those who are not effectively controlled. For instance, they may discover that patients do not have the money to buy their medication consistently and that they are unable to get transportation to the facility that offers their treatment. Once these factors are identified the case manager at the clinic can work to get over these hurdles.
I will finish this overview in the field of healthcare management for population and analytics by presenting two examples of organizations that have utilized the method correctly. In August 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, an organization with 10 primary care clinics as well as 61 physicians, and Dr. Scott Fields, the Vice Chair of Family Medicine at the same organization. The name of the webinar was “Improving Your Practice with Meaningful Clinical Data”. Two of the goals of the session were to establish the skills that comprised their Quality Data Team, including which members they were and the method of building a set of quality indicators.
The clinics were already collecting numerous types of information to share with various groups. For instance, they were reporting data for “meaningful use” and to commercial payers, as well as employees groups. They decided to gather all of this information and arrange it into scorecards that would serve individual physicians as well as practice managers at each clinic. Some of the data obtained was data on patient satisfaction as well as hospital readmissions data and obesity data. Scorecards for doctors were developed to meet the needs and requests of the individual physicians as well as for the entire practice. For example, a doctor might want to request his own scorecard which identified patients whose indicators for diabetes indicated that the patient was out of the prescribed limits for his diabetes. Knowing this, a physician could devote more time to improving the health of the patient.
Scorecards for the clinic showed the extent to which the doctors at the clinic managed patients suffering from chronic illnesses in general. By using predictive analytics, the staff of the clinic could determine what processes and procedures contributed to improving the health of patients. Offering more active case management may have been demonstrated to be beneficial to patients with multiple chronic conditions.
Mr. Cox and Dr. Fields also stated that the members of the quality data team were proficient in understanding access, structuring data into useful ways, and in presenting information to physicians effectively, as well as the extraction of data from a range of sources. The principal goals of the data team were to keep in balance the competing priorities of providing top-quality healthcare and ensuring that the operations were run efficiently and that the patient’s satisfaction was high.