Cross the Finish Line First
Cross the Finish Line First
May 29, 2013 by PSCI

The Affordable Care Act of 2010 (ACA) opens the door to a wealth of opportunities for hospitals and phy-sician groups. They are beginning to adapt to the new pay-for-performance and bundled payment systems and develop population-based care management programs.While the goal of ACA is to hold hospitals and physi-cians jointly responsible for quality and cost of care, the new payment models span the entire care continuum, including primary care physicians (PCPs), specialists, hospitals, post-acute care, and re-admissions. The big-gest winners will be those who can improve quality of care while driving down costs. Those that focus first on preventive care for top chronic illnesses will be the first to cross the finish line.


In order to bend the healthcare cost curve, providers must focus on preventive care for chronic care patients.


Innovative healthcare providers take the lead by developing coordinated care systems that embody the core principles of preventive care: Patient-Centric Medi-cal Homes (PCMHs) and accountable care organizations (ACOs). Physician networks are adopting the PCMH model, relying on PCPs and care coordinators as the central hub for care and looking to specialists when necessary. PCMHs deliver preventive care to the entire spectrum of patients, from healthy to chronic, with the goal of avoiding admissions to acute care facilities.


Why Chronic Conditions First.

Chronic diseases account for the majority of acute care costs (in-patient, out-patient, and emergency room [ER]). Controlling acute care admissions for chronic disease is essential to controlling healthcare costs. Therefore, the effort to minimize healthcare costs must begin with managing top chronic conditions.

According to the Centers for Disease Control and Prevention (CDC), “Chronic diseases are the lead-ing cause of death and disability in the U.S.," 1and Healthcare Cost Monitor underscores this fact, reveal-ing that “Seventy-six percent of Medicare spending is on patients with fi ve or more chronic diseases." The Agency of Healthcare Research and Quality (AHRQ) also emphasizes the high cost of chronic conditions.

Regardless of which payment model predominates (shared savings, bundled payment, or ACO), in order to bend the healthcare cost curve, providers must focus on preventive care for chronic care patients. Provider orga-nizations will need an innovative approach to redesign care processes, with a focus on keeping chronic care patients healthy and out of ERs and hospitals.

Yesterday: Claims-Based Predictive Models

For years, healthcare insurance companies (payers) have mined claims data for chronic patients and have built predictive models to identify high-risk patients. Armed with historical reports, case managers designed intervention programs that were meant to prevent com-plications among chronic patients and reduce ER visits and hospitalizations.

While this approach has seen some success, limita-tions far outweigh merits. Data used by payers to a high risk patients are historical claims data—primarily costs, admissions, and diagnoses. Because this view is retrospective and heavily biased toward cost, patients with past high acute care costs are .agged as “risky" regardless of their current state of health (SOH). Furthermore, regression and time series risk models are typically updated only annually.

Most physicians are highly skeptical of claims-based predictive models because they have no clinical basis and give no consideration to an individual’s current SOH. Moreover, there is a complete lack of causation: Why is a patient considered high risk. What are the clinical reasons. How do we lower the patient’s riskscore. How does the score measure the effectiveness of my care management program.


These models lack a correlation to clinical informa-tion. For example, a physician will acknowledge a high diabetes risk score if there is evidence that the patient has a high body mass index (BMI), and HbA1C tests have been consistently high over the past year and are trending higher. The score becomes even more credible when there is evidence of ER admissions or acute care inpatient (IP) admissions.


Unfortunately, an individual’s current SOH has no bearing on his or her claims-based risk score. Claims- based risk scores are created with regression analysis at a population level to predict scores at the patient level. Individual scores are relative to the population and could therefore change as the population changes, even with no change in the individual’s SOH.

Claims-based risk scores provide no insight for care at the provider level.

Not only are today’s calculations unsuitable for determining a patient’s true risk, they provide no insight into how an individual’s score improves or deteriorates after each clinical visit. Information lags far behind; physicians are given no insight to actively manage ongoing care. Claims-based risk scores are not actionablethey provide no insight for care at the provider level.

Claims-based risk scores are also deficient because they do not adequately represent the population. Reports provided by payers are used primarily by case managers, who most often work for a payer. Physicians reject these reports as a basis for their own effectiveness in managing patients because they are only a subset of their total population. Furthermore, payer reports are not typically useful for evidence-based care to identify and implement clinical best practices. Finally, they are inadequate for measuring physician performance to design incentive programs.

To use payer reports across an entire population, a provider would first need to normalize multiple payers’ risk-scoring systems, then aggregate the information. Because each payer has a unique reporting method, there is little chance that the resulting information would be accurate or meaningful for developing care management programs

Considering today’s approach to developing care management programs and understanding physician effectiveness, it’s important to remember that the Centers for Medicare and Medicaid Services (CMS) does not provide patient risk scores. Because Medicare patients account for the majority of chronic patients and populations, other payers’ risk reports incorporate only a small fraction of all chronic patients. Therefore, the impact of using individual (or even combined) claims-based payer risk reports is minimal in any effort to bend the overall patient population healthcare cost curve at the provider level.

A New Approach Disproportionate Focus on Existing Chronic Patients

The best approach is to monitor all patients, healthy and chronic, for risk of hospitalization. Unfortunately, current claims-based predictive risk models allow no room for this. The intervention approach is designed specifically for the chronically ill group, while wellness programs re.ect only the hope that the healthy population will remain healthy. Because today’s healthy patients are largely ignored, yet will become tomorrow’s chronic patients, this approach is deeply .awed. If providers delay uncovering and examining causes until a chronic diagnosis emerges, there is no opportunity to avoid a chronic scenario.

There is inherent con.ict between physicians and payers. Claims-based risk models create a grave professional con.ict for today’s physicians. To realize bonuses, they must choose cost of care over effective care. To make matters worse, incentives do not reward every healthcare professional who has an impact on patient health. Conversely, payers strive to minimize bonuses to physicians and networks. Physicians perceive that payers have an “upper hand," can deny bonuses as models change, and assert that costs are higher than “reasonable" against the statistical model

Progressive medical groups do not use claims-based patient risk reports created by payers to develop care management programs. And, until today, there has been a stark absence of credible decision support, hindering proactive care management. As a result, health professionals have not had the ability to focus on population SOH as a means to reduce ER and hospital admissions.

Vital Progress: Clinical Models for Population Management

In this age of electronic medical records (EMR), the healthcare industry is proclaiming a new wave of decision support for primary and acute care, leveraging data from EMR applications. The new generation of primary care management solutions delivers real-time, meaningful use clinical data from EMR records.

Today, most large physician groups and PCMHs already use at least a basic EMR system. CMS predicts that by 2014, more than 50 percent of all eligible medical professionals in the U.S. will use EMR. Torontobased Millennium Research Group predicts the EMR software market will grow at an annual rate of more than 12 percent through 2016, hitting $8.3 billion. This is a landmark paradigm shift because for the first time, clinical information is digitally available in real time,with reasonable availability of laboratory results.

These systems use patient medical records to measure SOH and evaluate the effectiveness of care programs and evidence-based medicine. Real-time clinical data from EMR records is also being used to create sustainable, repeatable programs to reduce the number of high-risk patients and design individualized care management programs. Using current clinical data for analysis rather than historical claim data means that healthcare providers create programs that are meaningful and effective for their specific populations.

The new care management decision support systems use actual clinical data, and there is little or no analysis or interpretation required by the physician. As a result, a care coordinator can take ownership of care management so that PCPs can focus on delivering patient care. In light of predictions for the short supply of doctors over the next few years, this is good news for patients and providers alike.

Closed-Loop Care Management Programs

Using real-time clinical data from EMR records, healthcare providers 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

Closed-Loop Care Management Program

Population SOH Stratification

The very foundation of the well-designed program is population SOH stratification, the ability to categorize patients into high (red), moderate (yellow), and low (green) risk groups by chronic condition (see Figure 2). Population stratification makes it possible to design customized programs for high-risk patients, execute and monitor programs, and measure the performance of clinical teams for incentive management.

SOH stratification provides actionable and measurable information about actual health status at the population and patient levels, with visibility of controllable and noncontrollable factors. An SOH model takes into consideration every patient’s age, gender, ethnicity, family history, all clinical factors (e.g., BMI, lipid panel, blood HM, HcA1C) and comorbidities,and delivers an accurate SOH score for every encounter and for the entire population (scores range from 0–100). A low score indicates excellent health, and as the number increases so does the likelihood of complication(s) and hospitalization within 12–18 months. SOH is a risk predictor.

It is also an indicator of the quality of care delivered. If the score trends down over time, the quality of care is good because health is improving. In this sense, the trend of the SOH score is a measure for quality of primary care.

While payers have their own calculations and definitions of risk, the remainder of this article uses the terms risk and state of health interchangeably. Origins of SOH Models Nationally accepted clinical models are the basis for SOH models. In some cases, when data did not contain all the parameters required to compute SOH scores, assumptions and approximations were considered and validated with physicians to ensure the integrity of the models. The SOH models were then validated against historical data.

Population SOH (Risk) Stratification by Physician; Focus on Prevention and Screening; Monitor Compliance for Chronic Conditions

CHF = congestive heart failure, CHD = coronary heart disease, Red = high risk, Yellow = moderate risk, Green = low risk.


SOH scores are calculated at the patient level and rolled up to a population level. In Figure 2, each row corresponds to a physician’s patient population. It shows the patient count, the number of office visits (encounters) and the average population SOH score for each chronic disease. Red signifies high-risk scores. Physicians and care coordinators use this easy-to-digest visual system to focus on high-risk populations and drill down to individual patients to understand factors that contribute to scores.

This approach allows healthcare providers to design meaningful preventive care programs for the exact population and create individualized programs for specific patients

Chronic Disease Management

Patients who comply with prescribed care programs are typically more successful in managing chronic conditions. This is where care coordinators play an important role. Leveraging SOH scores, care coordinators pinpoint high-risk patients by chronic condition, and best-evidence guidelines become the basis for customized care management programs. The care management program is integrated with the care management execution system that includes patient scheduling, outbound call centers, home visits, patient portals, and e-mails. While the disease management program identifies needs, the execution system promotes compliance with treatments, medications, scheduling laboratory tests, attending educational counseling sessions, and other prescribed activities.

Monitoring gaps in care established by evidencebased data, patient SOH trends, and underlying clinical drivers over time, care coordinators can identify patients who need their attention

Care Coordination

Physicians who improved SOH for their population (i.e., lowered the score) over a one- to three-year period established and used better clinical protocols (i.e., best practice care management programs). As demonstrated in Figure 3, one physician’s congestive heart failure (CHF) population risk increased to 55 percent, while another’s dropped to 5 percent. Analyzing SOH population trends by physician population, the clinical leadership team identified the most effective

Effectiveness of Tw o Physician CHF Populations

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

CHF = congestive heart failure, Red = high risk, Yellow = moderate risk, Green = low risk.


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.

Red = high risk, Yellow = medium risk, Green = low risk.


Physician Value Index Used for Incentive Management for Care Teams

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

Bubble Chart for Physician Dimension


clinical protocols for each specific patient population and standardized them as best evidence care guidelines. The physician team also used SOH scores as a measure of primary care quality, resource utilization, costs, and patient experience to establish best evidence care protocols, lower costs, and improve patient experiences.

Incentive Management

It is not enough to simply design and launch new programs. If financial incentives for healthcare profes sionals are not aligned with performance, success may be temporary and hard to sustain. Effective incentive programs clearly drive high quality and reduce costs for greater value in health care by:

  1. Aligning team incentives with population quality and cost performance targets (physicians and care coordinators)
  2. Establishing and sharing best evidence practices by chronic condition
  3. Encouraging teamwork to lower healthcare costs
  4. Illustrating accurate physician and clinical coordinator population performance and the impact on incentives

Incentive programs reward care teams for reducing population risk scores, improving patient satisfaction scores, and reducing overall patient costs. Continuum of care dashboards (ambulatory and acute) are useful in designing incentive programs and illustrate risk-cost-quality details for each patient (Figure 4).

Patient SOH scores can roll up to population averages. For example, one incentive program dash-board maps physician/care coordinator teams on a cost-quality grid (Figure 5). In this case, the quality metric captures population SOH, ACO quality mea-sures, and patient satisfaction scores. The intersection of the crosshairs is the target for quality and cost for the specific patient population. Each bubble corre-sponds 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.

Results: Validating the SOH Model Approach

Using the SOH scores as a measure for primary care quality and indicator of possible hospitalization

Relationship Between SOH and IP/ER Admissions for Chronic Conditions

SOH = state of health, CAD = coronary artery disease, IP = inpatient, ER = emergency room

Results: SOH Population Charts


Relationship Between SOH Composite and IP/ER Admissions for Chronic Conditions

SOH = state of health, ER = emergency room, IP = inpatient, TOT = total


Relationship Between SOH Composite and PMPM Costs

SOH = state of health


is a new concept and will become the contemporary paradigm for chronic disease management. There- fore, it is important to understand how effectively the model and scores could predict hospitalizations against historical patient population data. To validate the new SOH model, researchers (authors of this article) compared it with a leading claims-based risk model (payer model).

Ta ble 1 describes the cohort of population of 20,000 patients analyzed over a period of 1 year. The population had 917 IP admissions and 2,127 ER visits in the year 2010.The insurance payer used claims data (patient age, gender, ethnicity, previous ER, in-patient [IP] admissions, costs, diagnoses, and other claims files data) to calculate risk score as a number between 0–5,000.

For the SOH model, researchers used real-time clinical data (patient age, gender, ethnicity, vital signs, lab results, and treatment medications). The SOH model did not include past ER or IP admissions data. The SOH model calculated a risk score between 0–100 for four chronic conditions—type 2 adult diabetes, coronary artery disease (CAD), CHF, and asthma.

Next, researchers calculated an SOH score for each patient using historical data over two years (2008–2009) and stratified the population based on SOH scores. Researchers compared SOH scores to actual IP admissions and ER visits

Relationship Between IP/ER Admissions and SOHScore

Figure 6 shows the relationship between SOH scores and IP/ER admissions. The X axis shows SOH ranges. Y Axis shows the percentage of patients in the SOH range with IP/ER admissions. As the score in-creases, the admission within that band also increases. Thus, Figure 6 validates the accuracy and predictive power of the SOH score.

Creating an SOH Composite

Next, a weighted average Composite SOH was cre-ated using annual national average hospitalization costs by major chronic condition to calculate relative cost burdens. This cost represents the average inpatient ad-mission cost of treating a patient with the chronic con-dition. The relative cost burdens are shown in Table 2. The SOH composite effectively penalizes those patients who are at a higher risk for CAD than for asthma

Figure 7 shows the relationship between the com-posite score and admissions. Once again, we find that the higher the composite score, the greater the likeli-hood of an ER/IP admission.

SOH Validation

SOH = state of health


Relative Cost Burden for Each Chronic Condition Is the Weightage Given Based on the Average Hospital Bill per Admission for the Specific Chronic Condition


Relationship Between SOH and PMPM Costs

Next, average Per Member Per Month (PMPM) costs were calculated for the population belonging to each SOH band using only acute care admission and ER costs (primary care costs were ignored). Figure 8 shows the relationship between SOH scores and PMPM costs. Based on this, an improvement of SOH score of 1 percent translates to roughly a $7.50 reduction in PMPM costs.

Everyone Works Smarter Using SOH Models

Proactive Population Health Management

SOH models are accurate, predictive, and ideally suited for proactive population health management programs. Given the rapid adoption of EMRs among PCPs and medical groups, the data required to build SOH models is readily available now and will continue to expand over the next few years.

There are four types of population pools that every payer or self-insured employer needs to be aware of. The “Expected" are those cohort of patients who have high historical cost and poor health. They can be identi-fied by the traditional claims-based analysis. “Frequent Fliers" are those patients who are otherwise healthy but who show up at ER very frequently. “Hidden Op-portunity" are members who have poor health but do not (yet) have a history of ER/IP admissions. These members are future chronic patients and pose a contin-gent future liability on the payer, yet are not identified through claims-based analysis. Finally, the "Unknowns" are members that comprise both young and healthy members as well as members who don’t go for regular checkups; but are pre-diabetic and pre-hypertensive, and have a parental history of chronic diseases.

By segmenting a population into these groups, care coordinators, case managers, and wellness coordina-tors can create specific registries and prioritize patients based on SOH score and other criteria such as compli-ance and socio-economic status. The resulting health management programs are highly individualized and effective.

Proven Track Records

Healthcare providers can enable continuous improvement using SOH models together with care management programs. This approach already has proven track records in a number of leading PCMHs such as the Medical Clinic of North Te xas (MCNT). Within these organizations, a wide variety of individuals actively use these models in their daily work, including:

Administrators and management—to quantify

the effectiveness of care management programs, measure productivity, and monitor incentive programs

practices in managing disease, in line with their desire for evidence-based care; by analyzing SOH scores and understanding drivers, they have more insight to deliver better care

Care coordinators—to identify high-risk

patients, understand risk factors, develop individual care programs, and monitor patient compliance MCNT, with a Level 3 Recognition from the National Committee for Quality Assurance (NCQA)

Physician Practice Connections®–Patient Centered Medical Home™ (PPC-PCMH), has pioneered the SOH-based population management approach. MCNT experienced a stellar FY 2010 performance with Total Medical Cost trend. Their managed population of 2.4 percent better-than-market performance was the culmination of various quality-of-care drivers:

  1. Potential avoidable ER visits decreased by 13.3 percent.
  2. POP diagnostics trended only 1.9 percent vs. market trend of 9.7 percent.
  3. OP surgical trended 5.6 percent vs. market trend of 15 percent.
  4. Utilization of Certified Clinical Documentation Specialists (CCDSs) increased by 18.3 percent while drugs administered trended 10 percent less than market.

Chronic Diseases

CDC on Chronic Diseases

Seven out of 10 deaths among Americans each year are from chronic diseases. Heart disease, cancer, and stroke account for more than 50% of all deaths each year. In 2005, 133 million Americans—almost 1 of every 2 adults—had at least one chronic illness. Obesity has become a major health concern. One in every 3 adults is obese, and almost 1 in 5 youth between the ages of 6–19 is obese (BMI=95th percentile of the CDC growth chart). Diabetes continues to be the leading cause of kidney failure, nontraumatic lower-extremity amputations, and blindness among adults aged 20–74. Source:

AHrQ on Cost of Chronic Conditions

The 15 most expensive health conditions account for 44% of total healthcare expenses. Patients with multiple chronic conditions cost up to seven times as much as patients with only one chronic condition. Source:

Healthcare Cost Monitor on Chronic Disease Spending

Seventy-six percent of Medicare spending is on patients with 5 or more chronic diseases. Currently, 10% of healthcare dollars are spent on overall direct costs related to diabetes, amounting to $92 billion a year (1.5X the amount spent on stroke or heart disease). CDC predicts spending on diabetes care will reach $192 billion in 2020. According to theMilken Institute, overall cost of heart disease is predicted to reach $186 billion in 2023. Source:

  1. High tech scans/1,000 decreased by 12 percent.
  2. 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 in.ux of market changes


To lower health costs, physician networks and medical homes must employ a closed loop population management program that focuses on patient SOH (risk) stratification, chronic disease management, care coordination, and incentive management. This approach will enable them to consistently reduce ER and inpatient admissions, which are the greatest expenditures in health care today.

To become masters in their population management programs, they need decision support systems such as population SOH stratification and predictive models. With the growth of EMR systems, claims-based risk analysis will be replaced by clinically driven models that leverage up-to-the-minute clinical data to accurately determine SOH scores.

SOH scores are more accurate in relation to actual patient risk and have extremely strong predictive power. Because they are based on actual clinical datarather than claims history, they are widely embraced by the physician community. Physicians actively look to SOH models to understand causes, predict outcomes, and focus on controllable factors to improve patient health. Since EMR, laboratory, and pharmacy data is now widely available, SOH models are easy to build for most physician practices and PCMHs.

Ultimately, today’s physician has real power to bend the healthcare cost curve by focusing on high risk chronic patients, designing appropriate care management programs, and helping to keep patients out of hospitals.


  1. Centers for Disease Control and Prevention. 2010. Chronic Disease and Health Promotion. Accessed February 4, 2012 at
  2. Kimberly Swartz. 2012. Projected Cost of Chronic Diseases. N.d. Health Care Cost Monitor; The Hastings Center. Accessed February 4, 2013 at http://healthcarecostmonitor.the-
  3. M.W. Stanton. 2006. The High Concentration of U.S. Health Care Expenditures. U.S. Department of Health and Human Services (HHS), Agency for Healthcare Research and Quality (AHRQ). Accessed February 2, 2013 at

Lister Robinson, R.N., B.S., M.B.A., CPHQ, is director, clinical & quality operations, Medical Clinic of North Te xas, and Kirit Pandit, M.S., M.B.A., is president, PSCI Solutions, Allen, Texas.

Trending Speakers