population health management real time state-of-health analysis

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White Paper

April 2012

Population Health Management: Real-Time State-of-Health Analysis Solutions

Leveraging EMR Data to design and execute provider-driven care management programs.

The Affordable Care Act opens the door to a wealth of opportunity for hospitals and physician

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 this piece

of legislation is to hold hospitals and physicians jointly responsible for quality and cost of care, the

new payment models span the entire care continuum including primary care physicians, specialists,

hospitals, and other medical professionals. The biggest winners will be the ones 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 reach the finish line.

Innovative healthcare providers take the lead by

developing coordinated care systems that embody

the core principles of preventive care: Patient-

Centric Medical Homes (PCMHs) and Accountable

Care Organizations (ACOs). Physician networks

are adopting the PCMH model, relying on primary

care physicians (PCPs) and care coordinators as

the central hub for care, and looking to specialists

when necessary. Medical homes 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 ER).

Controlling acute care admissions for chronic disease is essential to control healthcare costs.

Therefore, the effort to minimize healthcare costs must begin with managing top chronic conditions.

According to the CDC, “Chronic diseases are the leading cause of death and disability in the US,”1

and Healthcare Cost Monitor underscores this fact revealing that, “Seventy-six percent of Medicare

1 "Chronic disease and health promotion." Centers for Disease Control and Prevention. Center for Disease

Control, 2010. Web. 26 Feb 2012. http://www.cdc.gov/chronicdisease/overview/index.htm.

CDC on Chronic Diseases

7 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 out of

every 2 adults – had at least one chronic illness.

Obesity has become a major health concern. 1 in

every 3 adults is obese and almost 1 in 5 youth

between the ages of 6 and 19 is obese (BMI ≥

95th percentile of the CDC growth chart).

Diabetes continues to be the leading cause of

kidney failure, non-traumatic lower-extremity

amputations, and blindness among adults, aged

20-74.

http://www.cdc.gov/chronicdisease/overview/index

.htm

http://www.cdc.gov/chronicdisease/overview/index.htm




spending is on patients with five or more chronic diseases.”2 The Agency of Healthcare Research

and Quality also emphasizes the high cost of chronic conditions.3

Regardless of which payment model becomes

predominant (shared savings, bundled payment, or

ACO), in order to bend the health care cost curve,

health care providers must focus on preventive

care for chronic care patients. Provider

organizations 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 complications among

chronic patients and reduce ER visits and hospitalizations.

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.

Because this view is retrospective and heavily

biased toward cost, patients with past high acute

care costs are flagged as “risky”, regardless of their

current state of health. 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 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?”

2 Swartz, Kimberly. "Projected Cost of Chronic Diseases." Health Care Cost Monitor. Health Care Cost Monitor,

n.d. Web. 26 Feb 2012. http://healthcarecostmonitor.thehastingscenter.org/kimberlyswartz/projected-costs-of-

chronic-diseases/.

3 Stanton, M. W.. "The High Concentration of US HealthCare Expenditures." Agency for Healthcare Research and Quality.

AHRQ, 2006. Web. 26 Feb 2012. http://www.ahrq.gov/research/ria19/expendria.htm.

Health Care Cost Monitor on Chronic Disease

Spending

Seventy-six percent of Medicare spending is on

patients with five or more chronic diseases.

Currently 10% of health care dollars are spent on

overall direct costs related to diabetes, amounting

to $92 billion a year (1.5 times the amount spent

on stroke or heart disease). The Centers for

Disease Control and Prevention predicts that

spending on diabetes care will reach $192 billion

in 2020.

According to the Milken institute, overall cost of

heart disease is predicted to reach $186 billion in

2023.

http://healthcarecostmonitor.thehastingscenter.org

/kimberlyswartz/projected-costs-of-chronic-

diseases/

AHRQ on Cost of Chronic Conditions

The 15 most expensive health conditions account

for 44 percent of total health care expenses.

Patients with multiple chronic conditions cost up to

seven times as much as patients with only one

chronic condition.

http://www.ahrq.gov/research/ria19/expendria.htm

http://healthcarecostmonitor.thehastingscenter.org/kimberlyswartz/projected-costs-of-chronic-diseases/








These models lack a correlation to clinical information. For example, a physician will acknowledge

a high risk score if there is evidence that the patient has a high BMI, and the HbA1C has been

consistently high over the past year and is trending higher. The score becomes even more credible

when there is evidence of ER admissions or acute care inpatient admissions.

Unfortunately, an individual’s current state of health 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, therefore could

change as the population changes, even with no change in the individual’s state of health.

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. Information

lags so far behind; physicians are given no insight to actively manage ongoing care. Claims-based

risk scores are not actionable – they 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 in most cases

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.

In order 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 methodology, 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 CMS does not provide patient risk scores.

The fact that Medicare patients account for the majority of chronic patients and populations, other

payers’ risk reports incorporate only a small fraction 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 health care cost curve at the provider level.

FURTHER CONSIDERATIONS FOR A NEW APPROACH

Current thinking and efforts create a disproportionate focus on existing chronic patients. The

intervention approach is designed specifically for this group, while wellness programs reflect only

the hope that the healthy population will remain so. Because today’s healthy patients are largely

ignored, yet will become tomorrow’s chronic patients, this approach is deeply flawed. If providers

delay uncovering and examining causes until a chronic diagnosis emerges, there is no opportunity

to avoid a chronic scenario. 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.


Claims-based risk models create a grave conflict 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 health care professional that has an impact on patient health. Conversely, payers strive to

minimize bonuses to physicians and networks. Physicians perceive that payers have an “upper

hand” and can deny bonuses as models change, and assert that costs were higher than

“reasonable” against the statistical model. As a result, there is inherent conflict between physicians

and payers.

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 state of health as a means to reduce ER and hospital

admissions.

VITAL PROGRESS | CLINICAL MODELS FOR POPULATION MANAGEMENT

Today, most large physician groups and medical homes already use at least a basic EMR system.

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

will use EMR. According to Frost & Sullivan, the ambulatory EMR market will explode to $3 billion

by 2013. 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.

In this age of 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.

These systems use patient medical records to measure state of health, 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 health care providers create programs

that are meaningful and effective for their specific population.

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 primary physicians 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,

health care 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.

The very foundation of the well-designed program is

population state of health stratification, the ability to

categorize patients into high (red), moderate

(yellow), and low (green) risk groups by chronic condition.

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.

Population State of Health (SOH) Stratification

State of health stratification provides actionable and measurable information about actual health

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

An SOH model takes into consideration every patient’s age, gender, ethnicity, family history, all

clinical factors (like BMI, lipid panel, blood HM, PFTs) and co-morbidities, and delivers an accurate

SOH score for every encounter and for the entire population (score ranges 0 to 100). A low score

indicates excellent health, and as the number increases so does the likelihood of complication(s)

and hospitalization within 12 to 18 months. 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. In this sense, the trend of the SOH score is a

measure for quality of primary care.

While payers have their own calculation and definition for “risk”, the remainder of this article uses

the terms “Risk” and “State of Health” interchangeably.

Origins of State of Health (SOH) Models

Nationally accepted clinical models are the basis for state of health models. In some cases, when

the 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.

Figure 1 - Closed-Loop Care Management Program


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. “Red” signifies “high risk” scores. Physicians and care coordinators use the easy-to-

digest visual information to focus on high risk populations, and drill down to individual patients to

understand factors that contribute to scores.

Figure 2 Population SOH (Risk) Stratification by Physician. Focus on prevention and screening; monitor compliance for chronic conditions.

This approach allows health care 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 state of

health 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 emails. 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 evidence-based care, patients’ SOH trends, and underlying

clinical drivers over time, care coordinators can identify patients that need their attention.

Care Coordination

Physicians who improved the state of health for their population (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% (Figure 3). Analyzing SOH population trend by physician population, the team of

physicians identified the most effective clinical protocols for the patient population and standardized

around best evidence care. The physician team also used SOH scores as a measure of quality of

primary care, resource utilization, costs, and patient experience to establish best evidence care

protocols, to lower cost and improve patient experience. (Figure 4).

Figure 3 - Effectiveness of two physician CHF populations. Use best practices within the risk group for evidence-based care coordination: medicines, treatment levels, frequency of visits; by risk group.


Figure 4 - Population Chart / Cost-Quality Grid, High-Medium-Low Risk Population performance: Map patients on quality and total cost across the continuum-of-care (ambulatory and acute). Identify optimal preventive care levels to minimize lifecycle cost over a time period by chronic condition.

Incentive management

It is not enough to simply design and launch new programs. If financial incentives for health care

professionals are not aligned 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:

Align team incentives with population quality and cost performance targets (physicians

and care coordinators)

Establish and share best evidence practices by chronic condition

Encourage teamwork to lower healthcare costs

Illustrate accurate physician and clinical coordinator population performance, and the

impact to 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 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.

Patient SOH scores can be rolled up to population averages. For example, one incentive program

dashboard maps physician/care coordinator teams on a cost-quality grid (Figure 6). In this case,

the quality metric captures population SOH, ACO quality measures, and patient satisfaction scores.

The intersection of the crosshairs is the target for quality and cost for the specific patient

population. 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.

Figure 6 – 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”.


Results | Validating the SOH Model APPROACH

Using SOH models as a surrogate for primary care quality and the indicator of possible

hospitalization is a new concept and will become the contemporary paradigm for chronic disease

management. Therefore, it was important to understand how effectively the model and scores

could predict hospitalizations against historical patient population data. To validate the models,

researchers compared the new SOH model against that of a leading claims-based risk model (the

payer model).

The insurance payer used claims data (patient age, gender, ethnicity, previous ER, IP admissions,

costs, diagnosis and other claims files data). They calculated a risk score, a number between 0

and 5000.

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 established a risk score between 0 and 100 for diabetes (Table

1).

Total diabetes patients

(type 1 and 2, complicated and uncomplicated) 737

Time period (2010) 1 year

IP admissions 53

ER visits 95

Table 1 - SOH Validation

Next, researchers calculated a 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.

Inpatient Admissions

Figure 7 shows total hospitalized patients as a ratio of the total diabetic patients for that SOH band.

For example, in the SOH band 50-60, 20% of all patients were hospitalized. As the score

increased, the ratio of patients within that band also increased. 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

Next, researchers compared the SOH results to the payer’s claims-based actuarial model. Figure 8

shows the relationship between the payer risk scores and IP admissions. In the 250-500 risk band,

the ratio of admitted patients is higher than the SOH model. Since most patients are in this band,

the predictive power of the payer’s model at low risk scores is diminished. 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.

ER visits

Figure 9 shows a similar comparison, SOH bands and ER visits. This comparison and the uniform

curve further substantiates the SOH model as a valid and accurate predictor of ER admissions.

Similarly, Figure 10 shows the payer’s model for ER admissions, which is clearly weak at both low

and high risk bands.


EVERYONE WORKS SMARTER USING SOH MODELS

State of health models are highly accurate 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 EMRs 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.

Healthcare providers can enable continuous improvement using SOH models together with care

management programs. This approach has already been institutionalized in a number of leading

medical homes like Medical Clinic of North Texas (MCNT). Within these organizations, there are a

wide variety of individuals who actively use these models in their daily work, and can be described

as:

Administrators & Management, to quantify the effectiveness of care management

programs, measure productivity, and monitor incentive programs.

Physicians, to define and/or leverage best 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, who are primarily interested in identifying high risk patients, to

understand risk factors, develop individual care programs, and monitor patient

compliance.

Medical Clinic of North Texas (MCNT), a Level 3 Recognition by the National Committee for Quality

Assurance (NCQA) Physician Practice Connections ® - Patient Centered Medical Home™ (PPC-

PCMH) has been a pioneer user of SOH based population management approach.

MCNT demonstrated a stellar FY 2010 performance with Total Medical Cost trend for their

managed population of 2.4% better than market, is a culmination of various quality of care drivers:

Potential avoidable ER visits decreased by 13.3%

OP Diagnostics trended only 1.9% vs. market trend of 9.7%

OP Surgical trended 5.6% vs. market trend of 15%

Utilization of CCD Specialists increased by 18.3% while drugs administered trended 10%

less than market

High tech scans/1000 decreased by 12%

Overall performance index improved in Facility Outpatient (-5%), Other Medical Services

(-6%) and Professional (-1%) relative to the market

An enviable performance considering the challenges the healthcare provider markets are facing

with influx of changes in the market.


SUMMARY

To lower health costs, physician networks and medical homes must employ a closed loop

population management program that focus on patient SOH 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 (risk) stratification and predictive models. With the growth of

EMR systems, claims-based risk analysis will replaced by clinically-driven models that leverage up-

to-the-minute clinical data to accurately determine state of health 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 data rather 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 medical homes.

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.

About PSCI

PSCI serves healthcare providers with decision support to analyze PERFORMANCE, identify

improvement STRATEGIES, stimulate CHANGE, and monitor IMPROVEMENT across DRGs, with

process- and outcome-centric intelligence, predictability and transparency in cost-of-care and

quality-of-care. We place powerful analysis capabilities at the fingertips of every hospital executive,

at a fraction of the cost of those historically available only to a few large HMO giants. Hospital

executives gain insight within days, not months or years, to meet Triple Aim objectives (cost,

outcomes, patient satisfaction), meaningful use requirements, and financial goals. PSCI transforms

healthcare CIOs from "data and technology officers" to "intelligence and insight officers" and helps

them avoid the hassle and cost of delay associated with multi-year electronic health record and

ICD-10 projects. PSCIs closed-loop decision support solutions break down silos across hospital

systems to deliver actionable intelligence and what-if predictive modeling for improving overall

hospital financial performance. For more information, please call (469) 261-4840, or visit

www.pscisolutions.com.

© 2012 PSCI, Inc. All rights reserved.

http://www.pscisolutions.com/

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