Population Level Health Management and Predictive Analytics

There has been much discussion of population health management coupled with predictive analytics recently in the health care field. Why? Most who are discussing these topics see it as a means of improving the health of patients while reducing the costs of doing so. Providing better care at lower costs is becoming necessary as payers are beginning to pay for quality outcomes as they move away from fee-for-service.

What is population health and how does predictive analytics fit in? Let me begin by defining population health and illustrate predictive analytics. In statistics, population refers to the complete set of objects of interest to the investigation. For instance, it could be the temperature range of adolescents with measles. It could be the individuals in a rural town who are prediabetic. These two are of interest in healthcare. Population also applies to any other field of research. It could be the income level of adults in a county or the ethnic groups living in a village.

Typically, population health management refers to managing the health outcomes of individuals by looking at the collective group. For instance, at the clinical practice level, population health management would refer to effectively caring for all the patients of the practice. Most practices segregate the patients by diagnosis when using population health management tools, such as patients with hypertension. Practices typically focus on patients with high costs for care so that more effective case management can be provided to them. Better case management of a population typically leads to more satisfied patients and lower costs.

Population health from the perspective of a county health department (as illustrated in last month’s newsletter) refers to all the residents of a county. Most services of a health department are not provided to individuals. Rather, the health of residents of a county is improved by managing the environment in which they live. For instance, health departments track the incidence of flu in a county in order to alert providers and hospitals so that they are ready to provide the levels of care needed.

You should be able to see that the population whose health is being managed depends upon who is providing the service. Physician practices’ population is all the patients of the practice. For county health departments it is all residents of a county. For the CDC it is all residents of the United States.

Once the population is identified, the data to be collected is identified. In a clinical setting, a quality or data team is most likely the body that determines what data should be collected. Once data is collected, trends in care can be identified. For instance, a practice may find that the majority of the patients who are identified as being hypertensive are managing their condition well. The quality team decides that more can be done to improve the outcomes for those who do not have their blood pressure under control. Using the factors from the data that it has collected the team applies a statistical approach called predictive analytics to see if can find any factors that may be in common among those whose blood pressure is not well managed. For instance, they may find that these patients lack the money to buy their medication consistently and that they have trouble getting transportation to the clinic that provides their care service. Once these factors are identified, a case manager at the clinic can work to overcome these barriers.

I will finish this overview of population health management and predictive analytics with two examples of providers using the approach correctly. In August 2013 the Medical Group Management Association presented a webinar featuring the speakers 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 and 61 physicians, 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”. Two of the objectives of the webinar were to define the skill set of their Quality Data Team, including who the members were, and describing the process of building a set of quality indicators.

The clinics were already collecting a large variety of data to report to various groups. For instance, they were reporting data for “meaningful use” and to commercial payers as well as employee groups. They decided to take this data and more and organize it into scorecards that would be useful to individual physicians and to practice managers at each clinic. Some of the data collected was patient satisfaction data, hospital readmission data, and obesity data. Scorecards for physicians were designed to meet the needs and requests of the individual physicians as well as for the practice as a whole. For instance, a physician could ask to have a scorecard developed for him that identified individual patients whose diabetes indicators showed that the patient was outside of the control limits for his diabetes. Knowing this, a physician could devote more time to improving the quality of life of the patient.

Scorecards for the clinic indicated how well the physicians at the site were managing patients with chronic conditions as a whole. With predictive analytics the staff of the clinic could identify which processes and actions helped improve the health of the patients. Providing more active case management may have been demonstrated to be effective for those with multiple chronic conditions.

Mr. Cox and Dr. Fields also stated that the quality data team members were skilled at understanding access, structuring data in meaningful ways, at presenting data to clinicians effectively and in extracting data from a variety of sources. The core objectives of the data team were to balance the competing agendas of providing quality care, making sure that operations were efficient and that patient satisfaction was high.

A second example of population health management focuses on preventing cardiovascular disease in a rural county in Maine-Franklin County. Over a 40-year period, starting in the late 1960’s, a volunteer nonprofit group and a clinical group worked together to improve the cardiovascular health of the residents of the county. As the project advanced, a hospital joined in the efforts.

At the beginning of the prevention efforts, the cardiovascular health of this poor county was below the state average. As volunteers and clinical groups became more active in improving the health of its residents, various cardiovascular measures improved significantly and actually were better in some respects than more affluent counties in the state that had better access to quality health services. The improvements were driven by volunteers who went out into the community to get those identified as being at risk of developing cardiovascular problems involved in smoking cessation classes, in increasing their physical activity and in improving their diets. This led to lowering blood pressure, lowering cholesterol rates and improving endurance.

The results and details of this 40-year effort in Franklin County has been published in the Journal of the American Medical Association in January 2015. The article is “Community-wide CVD prevention programs linked with improved health outcomes”.

As you can see, a population level approach to healthcare provides effective results. A clinic can improve the outcomes of its patients with chronic diseases while balancing costs through improved efficiency by focusing on data at the population level. A community can improve the lives of its residents by taking a population level approach to preventive care. Population level approaches to healthcare are varied and can be very successful if population level theory is correctly implemented. Better results can be obtained pairing it with predictive analytics.

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Headaches and Natural Health Care

We know that there are side effects and dangers to drug therapy. Are there any safe alternatives to the medical approach to headaches? Safe, hands on therapies like chiropractic, acupuncture and even massage can offer the headache sufferer relief that is safe and free from side effects.

According to research appearing in the British Medical Journal (2004; 328(7442):744), acupuncture may be beneficial to headache patients. The study involved 401 subjects who regularly experienced headaches several times each week. Patients were divided into two groups. One group received 12 acupuncture treatments during a three month period. The other group did not.

The patients receiving the acupuncture experienced a reduction in the number of headaches-22 fewer, on average, over the course of a year. The acupuncture group also had a decreased number of doctors’ visits (by 25%), had 15% fewer sick days and took 15% less medication than the control group. There was also a reduction in the severity of the headaches for those who received acupuncture.

Research appearing in the journal Headache (2006; 46(10): 1492-502) tested the response migraine patients had to 12 weeks of treatment with acupuncture. Subjects of the study were 114 patients with a history of migraine headaches. For four weeks prior to the beginning of the study the subjects kept a diary and recorded information about their headaches. After the four weeks, the subjects were randomly divided into two groups. For twelve weeks, one group received drugs commonly prescribed to prevent migraines (metoprolol). The other group received between eight and 15 acupuncture treatments. Two of the subjects receiving the acupuncture dropped out of the study, while 18 of the subjects receiving the drug therapy dropped out of the study; some of those receiving the drug had side-effects, one patient actually got worse and quit using the drug.

In the group receiving the acupuncture, 61% had 50% or fewer migraine attacks. In the group receiving the drug, 49% had 50% or fewer migraine attacks. During a follow-up 12 weeks after the study, 70% of the acupuncture patients assessed themselves as being “good” or “very good”, compared to 49% of the patients who received the drugs.

After 24 weeks, only 26% of the drug group reported doing “good” or “very good” compared to 56% of the acupuncture group. In this study, acupuncture compared favorably to drugs for the prevention of migraines.

127 subjects were involved in this controlled study published in the Journal of Manipulative and Physiological Therapeutics (Feb. 2000:23(2), pp91-5). The subjects all had at least one migraine headache per month, based on the International Headache Society standard. The response of the group receiving treatment showed improvement in migraine duration, frequency, and disability. Those receiving chiropractic treatments used less medication than the control group. Of those receiving chiropractic adjustments, 22% reported a greater than 90% reduction in migraines after just 2 months of care. Additionally, another 50% of the group receiving chiropractic adjustments reported significant improvement in the severity of migraines.

Other scientific studies have demonstrated that chiropractic care is safe and effective in treating headaches. A review of research articles appearing in the Journal of Manipulative and Physiological Therapeutics (2001;24:457-466) found that spinal manipulation was beneficial to headache patients.

Even massage therapy is useful. Research appearing in the American Journal of Public Health (October 2002;92(10):1657-1661) showed that massage therapy could reduce the duration of headaches.