Population Health Management:Your Current State of Health Analysis презентация

claims data for chronic patients and have built predictive models to identify high-risk patients.

While this approach has seen some success, limitations far outweigh merits.

Data used by payers to flag high risk patients is historical claims data — primarily costs, admissions, and diagnoses. Furthermore, regression and time series risk models are typically updated only annually.

they have no clinical basis, and give no consideration to an individual's current state of health.

Moreover, there is a complete lack of causation, "Why is a patient considered high-risk? What are the clinical reasons for the score? How do we lower the patient's risk score? How does the score measure the effectiveness of my care management program?“
http://healthcarecostmonitor.thehastingscenter.org/kimberlyswartz/projected-costs-ofchronic- diseases/
http://www.ahrq.gov/research/ria19/expendria.htm

are created with regression analysis at a population level to predict scores at the patient level.

Not only are today’s calculations unsuitable for determining a patient’s true risk, they provide no insight on how an individual’s score improves or deteriorates after each clinical visit.

chronic patients.

A better approach is to monitor all patients, healthy and chronic, for risk of hospitalizations.

Unfortunately, current claims-based predictive risk models allow no room for this approach.

at least a basic EHR system.

CMS predicts that by 2014, more than fifty percent of all eligible medical professionals in the U.S. will use EHR.

This is a transformational shift, because for the first time in history, clinical information is digitally available in real time, with reasonable availability of laboratory results and patient vital data.

now have the capacity to design a closed-loop population care management program (Figure 1). A well-designed program delivers primary care to drive higher quality, reduce costs, and deliver greater
value in health care.

about actual health status at the population and patient levels, with visibility of controllable and non-controllable factors.

SOH is a “risk predictor”. However, it is also an indicator of the quality of care delivered.

If the score trends down, the quality of care is good,
because health is improving.

state of health models.

SOH scores are calculated at the patient level and rolled up to a population level (Figure 2).

In this example, each row corresponds to a physician's patient population. It shows the patient count, the number of office visits (encounter) and the average population SOH score for each chronic disease.

more successful in managing chronic conditions.

This is where care coordinators play an important role.

Monitoring gaps in care established by evidence-based care, patients’ SOH trends, and underlying clinical drivers over time, care coordinators can identify patients that need their attention.

(i.e. lower the score) over a one to three year period established and used better clinical protocols (i.e. best practice care management programs).

In one instance, one physician’s CHF population risk increased to 55%, while another’s dropped to 5%.

within the risk group for evidence-based care coordination: medicines, treatment levels, frequency of visits; by risk group.

continuum-of care (ambulatory and acute). Identify optimal preventive care levels to minimize lifecycle cost over a time period by chronic condition.

with performance, success may be temporary and hard to sustain.

Effective incentive programs distinctly drive higher quality and reduce costs for greater value in health care.

Incentive programs reward care teams for reducing population risk scores, improving patient satisfaction scores, and reducing overall patient costs.

incentive programs and illustrate risk-cost-quality details for each patient (Figure 5).







Figure 5 - Continuum of Care Analysis by Patient, Preventive Care Impact on Acute Care Costs

Monitor how much total inpatient and outpatient care (cost and quality) is being provided to the risk panel; identify patient outliers.

example, one incentive program dashboard maps physician/care coordinator teams on a cost-quality grid.

Each bubble corresponds to a specific physician- care coordinator team, and the size of the bubble illustrates the size of the population they manage. The distance of each bubble from the crosshair indicates the positive or negative variance from the target and is proportional to each team’s bonus or penalty.( Refer Fig.6)

care teams.

Report shared savings by plan by physician on a periodic basis and show the impact of actions on their “pocketbook”.

new SOH model against that of a leading claims-based risk model (the payer model).

For the SOH model, researchers used real-time clinical data. The SOH model did not include past ER or IP admissions data.

Next, researchers calculated a SOH score for each patient using historical data over two years

the total diabetic patients for that SOH band.

At very high scores, all patients were hospitalized. Thus, Figure 7 validates the accuracy and predictive power of the SOH score.

Figure 7- Ratio of Hospitalized Patients to Total Diabetic Patients

risk scores and IP admissions.

Similarly, at higher risk scores, the predictive power of the payer’s model
is only 50% whereas the researchers’ SOH model is closer to 100% accurate

Figure 8 - Relationship between the payer risk scores and IP admissions.

and predictive, and ideally suited for chronic care population management by chronic condition.
Using SOH scores, care coordinators can correctly identify and focus on high risk patients with a great risk of hospitalization in the short term.

Given the rapid adoption of EHRs among primary care physicians and groups, the data required to build SOH models is readily available now, and will continue to expand over the next two years.

care management programs. This approach has already been institutionalized in a number of leading medical homes like Medical Clinic of North Texas (MCNT).

stellar FY 2010 performance with Total Medical Cost trend.

Overall performance index improved in Facility Outpatient (-5%), Other Medical Services (-6%), and Professional (-1%) categories, relative to the market. An enviable performance considering the challenges healthcare provider markets are facing with the influx of market changes.

a closed loop population management program that focus on patient SOH stratification, chronic disease management, care coordination and incentive management.

To become masters in their population management programs, they need decision support systems such as population SOH (risk) stratification and predictive models.