Volume 2, Number 5 May 2009
Hospital Contracts: The Critical
In This Issue
Elements of Financial Performance
Modeling 1 Hospital Contracts: The
Critical Elements of
Reconciliation between different data sets minimizes misunderstandings Financial Performance
by Terri L. Welter, Principal, and Charles A. Brown, Senior Manager, ECG
Management Consultants Inc., Arlington, VA 1 Care Management, Not
ospitals and managed care organizations are taking a more collaborative DM, Needed to Control
H approach to ensuring that negotiated terms produce the financial results that
they expect. In the current reimbursement environment, it is common for new
Costs, Improve Quality
contracts to include significant methodology changes as the parties move toward Population
episode-based payments. The development of an accurate and flexible contract Management Solution
model is critical to understanding the expected financial performance of negotiated Uses PM to Target
terms, particularly as reimbursement methodologies change. Modeling errors can Interventions
result in reimbursement differences when compared to projections – with the potential
impact of millions of dollars in either direction under a single contract. 8 Thought Leader’s
In order to successfully negotiate a new agreement and create financial predictability,
the parties must accurately model the impact of proposed terms. That typically 9 Industry News
involves applying the proposed methodology and terms to historic patient claims data
and comparing the projected payments to expected reimbursement under the current 11 Subscriber’s Corner
arrangement. While the concept is simple in nature, in practice, the complexities of
hospital billing and data capture result in significant differences between data sets 12 Catching Up With…
used by MCOs and hospitals. Rong Yi
Failure to appreciate the intricacies and subtle differences between the data sets and
adjudication interpretations can result in unintentional but material misrepresentations
of the financial impact of a proposed agreement. When those problems are
uncovered, they complicate negotiations by inserting a measure of mistrust.
continued on page 6
Care Management, Not Disease Management,
Needed to Control Costs, Improve Quality
How to meet patient needs while avoiding unwarranted treatment variations is the question
by Russell A. Jackson
isease management has the power to keep a lid on spiraling healthcare costs by keeping patients with chronic
D conditions as close to well as possible. But proving that disease management programs can do so cost-effectively
has been difficult, especially in the Medicare fee-for-service population. The predictive modeling solutions that
populate the tools that target patients for disease management intervention have a stake, of course, in the resolution of
The problem, says Health Dialog -- a leading provider of care management and analytic services and a wholly owned
subsidiary of Bupa, a global provider of healthcare services based in London -- is traditional DM programs only address
12% of the causes of unwarranted health cost and quality variations in the US healthcare system. That won’t be enough
for the Obama Administration, the company points out, which has upped the emphasis on cost containment and quality
Care management, the company says in a recent white paper called “Care Management: What Works,” has evolved into
an enhanced solution to the problem. It, according to the white paper, addresses “the other 88%.”
continued on page 4
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4 Predictive Modeling News May 2009
American Health’s Population Management Solution Uses PM…continued
Participants who meet the criteria for participating in lifestyle coaching receive education and support to develop personalized
plans for improving nutrition, implementing and adhering to a fitness program, managing stress and modifying other lifestyle
behaviors that impact health and well-being.
Predictive Modeling News talked to Reed about the disease management and lifestyle & wellness programs – and about
predictive modeling’s key role in both.
Predictive Modeling News: Is the predictive modeling component of the program tied directly to the health assessment tool?
Or does it also process data from the home monitoring devices and the personal tracking services available through the online
Jan Marie Reed: AHH relies on daily and weekly downloads of medical and prescription drug claims data into the predictive
modeling tool to identify persons with chronic conditions who may benefit from the disease management program. The health
assessment tool is also used to identify persons with DM-manageable conditions by virtue of the member indicating on the
health assessment that he or she has one or more of the targeted chronic diseases. The value of relying on both sources --
healthcare claims and the health assessment -- is it allows AHH to cast the widest net possible for identifying potential
participants for the DM program and to do so in a timely manner.
The predictive modeling tool also allows AHH’s nurse coaches to monitor DM participants’ compliance with nationally
recognized evidence-based care guidelines. For example, a nurse coach can assess whether a diabetic patient has
undergone a semi-annual glycated hemoglobin test and can counsel the participant accordingly.
Data from the home monitoring devices, personal tracking services and at-home biometric test results are downloaded into
AHH’s proprietary software, i-Suite, and are readily accessible to DM nurse coaches. The coaches reference each
participant’s data values from the disease-specific equipment and incorporate that information into their coaching sessions
with the participant. At any point in time, the nurse coach can not only assess the participant’s biometric readings, such as
blood glucose levels or daily weights, but can also determine the extent to which the participant is compliant with the testing
frequency recommended by his or her doctor. That additional information further enhances the education and clinical support
provided to participants.
PMN: Are the data used to populate a predictive modeling solution fully valid if they’re entered, unsupervised, by patients? Are
doctor-derived data more reliable?
JMR: We have learned through experience that self-reported results are not reliable and, therefore, the AHH program has
addressed that shortcoming. The biometric data used to populate i-Suite are automatically downloaded via the disease-
specific equipment DM participants receive from AHH. The equipment calculates the applicable readings and sends those
electronic readings automatically to AHH; therefore, there is no opportunity to falsify the information. The data supplied
through the disease-specific equipment are as reliable as physician-derived data.
PMN: What kind of response are you seeing to the new product launch? What kinds of companies are clients of the service?
Do you get more employers or more health plans as clients?
JMR: AHH is successfully moving its current client base to the new program. In these economic times, employers in all arenas
realize the need to improve the health status of their populations, which will ultimately yield lower healthcare costs. AHH has
third-party administrator customers, health plan customers and direct employer groups. The program incorporates many
components, including webinars, “Lunch & Learns” and coaching for disease management and lifestyle challenges. Various
components are appropriate for different groups. The components are “building block” components that allow our customers to
expand the programs to offer cost-containment initiatives to the full population of employers.
PMN: Does your company also offer the disease management and wellness programs that the predictive modeling component
indicates that patients might benefit from? If not, how do you direct patients to those programs?
JMR: AHH offers a full range of products under its population health management umbrella. AHH’s PHM product line offers
resources and tools to all persons, regardless of where they are on the health continuum – from healthy persons with no
health risks to members identified as “at-risk” for developing chronic conditions to individuals already diagnosed with diseases.
AHH’s lifestyle & wellness product serves all members, regardless of health status, but is most effective at keeping healthy
people healthy and preventing those at risk from developing diseases. AHH’s disease management product provides
comprehensive education, support, tools and resources to individuals with one or more targeted chronic conditions, such as
diabetes and heart disease. The predictive modeling tool, the health assessment and the biometric values play a role in
identifying members for the appropriate health management products and services.
AHH is a multi-URAC-accredited medical management firm that provides case management, utilization management, disease
management, lifestyle & wellness programs, maternity management, medical review, hospital bill auditing and pharmacy
benefits management. Offerings also include case management specialty services that encompass neonates, organ
transplants and oncology services. Visit www.americanhealthholding.com.
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6 Predictive Modeling News May 2009
Care Management, Not DM, Needed…continued Care Management, Not DM, Needed…continued
Those results are well-supported by rigorous plausibility inconsistencies and lags in data, most are predictable
testing of Health Dialog practices. Those plausibility tests, and can be factored into analytic approaches. In
intended to support internal learning and quality some instances, including the Medicare fee-for-
improvement, utilize formal study processes with service population, data problems are tremendous
randomization, and each demonstrates the powerful impact and work-arounds complex. Examples of potential
on medical costs of robust support for individuals with key problems that restrict program performance include
risks. massive, unstructured errors in member identification
data; large amounts of missing claims data; and
A series of tests of different types of outreach for individuals significant claims lag problems.
at risk for back and joint surgery, for example, demonstrated
that populations with much higher levels of health coaching • Lack of real-time data feeds for urgent needs.
had significantly lower surgical rates and significantly lower
overall health costs. A second test of different outreach • Limited or poor pharmacy information. Pharmacy data
approaches for individuals with chronic conditions, who had are extremely useful for identifying individuals with
a high likelihood of future costs but who had not yet been conditions, for developing appropriate targeting
reached by traditional outreach processes, demonstrated strategies and for enabling coaches to support
that populations with much higher levels of health coaching individuals in need. With poor pharmacy data,
had significantly lower overall health costs. program effectiveness is constrained.
Certainly there are care management programs that do not
work, which has been most widely discussed around the
Medicare fee-for-service population, but which can occur in Hospital Contracts: The Critical Elements of
other populations as well. Problems that care management Financial Performance Modeling… continued
programs can face in such instances include program design When the problems are not uncovered prior to contract
constraints and systemic data limitations. Health Dialog’s execution, the negative financial consequences can be
analysis shows that programs that do not reduce medical significant. Recognizing the risks and planning for
costs are subject to one or more of these constraints: appropriate data reconciliation thus become important parts
• Enrollment or opt-in requirements. Individuals with of the negotiation process.
progressive chronic conditions are poor at predicting
future needs. Enrollment or opt-in requirements bar Case Study: Overview
access to initially unreceptive people who might pose This case study is based on an actual negotiation between
tremendous opportunities for impact in the future. a large Midwestern hospital and an MCO. The MCO
• Limited or no provider touch points. proposed a new agreement that changed the inpatient
payment methodology from a percentage-of-charge
• Narrow focus on clinical quality improvement.
reimbursement scheme to All Patient Refined-Diagnosis
Improving clinical quality as currently measured may
Related Group case rates. The data and adjudication rules
have little to no impact on short-term medical costs
used by the MCO in its initial modeling resulted in a
because it focuses on effective care, where just 12%
material misstatement of expected contract performance.
of dollars are spent, and because its initial goal is to
The hospital was able to perform a reconciliation that
increase use of that care. A common mistake is to
resulted in a mutually acceptable data set that both parties
overemphasize those types of measures at the
agreed resulted in a reasonable projection of the contract’s
expense of ensuring that people with chronic and
other high-risk conditions avoid excess supply-
sensitive care and get the right preference-sensitive Key Actions Taken
care – where 88% of the money is spent. The main
reason that people with chronic conditions can be so Because the hospital did not have the ability to group
costly is their conditions cause them to interact claims into APR-DRGs, the MCO provided a model using
frequently with the medical system. Thus, they are historic claims data and calculated a transitional base rate
more at risk to be drawn into the other 88% than are estimated to be revenue-neutral. After an initial high-level
healthy individuals. analysis, it became apparent that there were significant
differences between the hospital’s and MCO’s data. A logic
• Single- or limited-condition programs. The largest test compared total charges modeled by the health plan
opportunities for cost savings can be discovered with the hospital’s claims data for the same time period.
when the entire population is available for potential The hospital’s data included $4 million in charges – 14% --
intervention. A focus on one or two diseases more than what the MCO’s model contained. An in-depth
constrains the overall impact significantly. reconciliation of the hospital and MCO data was needed
• Data sources that are inconsistent or untimely. Data before a base rate could be calculated that both parties
are the life’s blood of a care management program. agreed would yield revenue-neutral reimbursement. In
Without reliable and timely data sources, addition, the initial review of the data and modeled
programmatic focus is off, front-line support payments compared to the contract terms resulted in
processes are ill-advised and course corrections are identification of errors in the way the MCO model treated
difficult. While it is expected to have small some of the claims
continued continued on page 7
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8 Predictive Modeling News May 2009
Hospital Contracts … continued
Differences in the data sets and modeling assumptions can result in a material misstatement of financial performance. It is
paramount that MCOs and hospitals reconcile their data sets, assumptions and contract models during negotiations so that the
intended financial consequences are realized.
ECG offers a broad range of strategic, financial, operational and technology-related consulting services to healthcare
providers. The firm has extensive experience assisting hospitals and medical groups in different regions of the country in
commercial health plan contract analysis, negotiation and revenue capture and boasts expertise in every step of the
commercial health plan process, including defining the contracting strategy, modeling the revenue impact of contract terms,
negotiating contracts and resolving payment disputes. Visit www.ecgmc.com.
Contact Welter at firstname.lastname@example.org or Brown at email@example.com or either at 703-522-8450.
Thought Leader’s Corner
Each month, Predictive Modeling News asks a panel of industry experts to discuss a topic suggested by a subscriber. To
suggest a topic, send it to us at firstname.lastname@example.org. Here’s this month’s question:
Q: “What categories of high-risk patients have you found predictive modeling
applications the most successful, and least successful, at identifying?”
“Employer groups want to understand how they can use predictive modeling to reduce healthcare costs as well as efficiently
provide appropriate healthcare to their employees at the optimal point in time. Health plan clients are using predictive
modeling to inform employer groups regarding medical cost drivers and to facilitate coordination of care for subsets of
employees. Examples include:
• Describing member population demographics by condition or diagnosis predicted with high forecasted cost compared to
the prior time period -- e.g., since renewal or the prior calendar year.
• Members who have gaps in care. Describing subsets of the employee population compliant with preventive healthcare
• Quantifying employee subsets that have currently low healthcare expenditures but that are predicted to have higher
costs in the next 12 months. That empowers employer groups and payers alike to appropriately identify members for
case or disease management programs. Health plans use the predictive modeling risk stratification to place members in
risk categories or levels for appropriate care management interventions.
• Structure customized benefit plans for employees. Employer groups may agree to a health plan’s recommendation to
focus more intensive clinical resources on predictive modeling-identified employees with diabetes-related care gaps
who are expected to have higher inpatient or emergency room utilization within the next 12 months.
• Demonstrate to employer groups that predictive modeling provides a means to identify their ‘well associates’ who
require educational/informational health literature outreach or a ‘light’ touch by a case or disease manager.”
President, MEDai, Inc.
“We have found that predictive modeling is effective at predicting future high-cost patients. However, high-cost patients are
frequently unmanageable by the type of programs that a plan has in place. For that reason, Solucia has developed a
predictive model ‘overlay’ that we incorporate into our financial predictions that takes into account additional factors:
intervenability, the type of care management program and the program cost.
Thus, a member’s risk score is adjusted to incorporate the intervenability score, resulting in high-risk predictions that are
more likely to be amenable to management. For the purpose of re-scoring members, we define ‘intervenability’ as the
percentage of a member’s predicted costs that are likely to be amenable to management by the program. Thus, a member
who is high-risk because of, say, cancer, may have a low intervenability score, because there may be few things that a
program can do to reduce the cost of normal care. On the other hand, a patient with multiple chronic conditions may be
intervenable because a program may be able to impact costs. “
Ian Duncan FSA FIA FCIA MAAA
President, Solucia Inc.
continued on page 9
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10 Predictive Modeling News May 2009
Carefx Reports 200% Year-Over-Year Revenue Growth
Rehab Firm to Study Predictive Tools for Nursing
Carefx Corp., Scottsdale, AZ, reports 200% revenue growth Home Falls
in 2008 and increased penetration of the hospital, health Northbrook, IL’s Simply Rehab LLC has launched a multi-
system, integrated delivery network and health information state project to help operators of skilled nursing facilities
exchange markets, with an 80% increase in client bookings and assisted living facilities improve their ability to predict
and the addition of some 200 hospitals to its roster. “We see which residents are at risk for falling. The program also
our strong growth as continued validation that organizations includes measurements that lead to appropriate
can no longer settle for proprietary or monolithic systems preventive actions for the at-risk residents, the company
that do not support clinical workflow or cognitive tasks and says. Falls among community-dwelling older adults are a
force clinicians to sift through enormous bundles of data significant problem in the US, it adds, with Medicare’s cost
from disparate sources,” says Andrew Hurd, chair and CEO of treating the injuries resulting from falls approaching $3
at Carefx. “We built the Fusionfx solution suite to address billion a year. Statistics gathered by the Centers for
the information gaps that occur between disparate systems Medicare and Medicaid Services indicate that for every 10
to simplify clinical and business workflow and to ultimately residents of nursing homes in the United States, five falls
improve the quality of care delivery.” are reported each year. In the study, nursing homes in
Hurd’s comments are echoed, according to the company, in Illinois, Colorado and North Carolina will use a predictive
a January 9, 2009, National Research Council report, which methodology that incorporates “a unique collection of
offers a critical evaluation of current healthcare IT solutions’ elements” from the popular Berg Balance Scale Test and
performance in the areas of data sharing, integration and “improved predictive techniques” that include the Alternate
management. The NRC’s “Computational Technology for Step Test, the Sit-to-Stand-5-Times Test and the Six-
Effective Health Care: Immediate Steps and Strategic Meter Walk Test. In one study, those methodologies
Directions” advises organizations to “develop the necessary proved to be superior to other popular techniques at
data infrastructure for healthcare improvement by predicting who would be a multiple faller. Robert Kunio,
aggregating data concerning people, processes and project manager and chief operating officer at Simply
outcomes from all sources” and to “organize incentives, Rehab, says skilled nursing facilities and assisted living
roles, workflow, processes and supporting infrastructure to facilities are under enormous pressure to reduce the
encourage, support and respond to opportunities for clinical frequency and cost of falls among their residents. “Rather
performance gains.” Visit www.carefx.com. than focusing our efforts on helping our clients better react
to falls,” he comments, “we have decided to concentrate
our efforts on better predicting who is at risk and then
taking appropriate preventive measures.” Adds Elizabeth
Roth, president there: “We have now assembled and
begun testing a collection of improved predictive
Mayo Study Shows Finger Device May Help Predict techniques for both the ambulating population and the
Future Heart Events propelling population. Our database is giving us a good
Results of a Mayo Clinic study show that a simple, non- correlation between quarterly predictive scores and actual
invasive finger sensor test is “highly predictive” of a major falls, which then allows us to fine-tune the protocol on a
cardiac event, such as a heart attack or stroke, for people regular basis for various segments of the geriatric
who are considered at low or moderate risk, according to population. The outcome of the effort should be an overall
researchers there. The device, called the EndoPAT, by reduction of falls and the cost of falls to our clients, to
Itamar Medical, measures the health of endothelial cells by Medicare and ultimately to taxpayers.” Simply Rehab
measuring blood flow, a statement from Mayo explains. provides physical, occupational and speech therapy
Those cells line blood vessels and regulate normal blood services to nursing homes and exercise programs to
flow, and research has shown that if they don’t function assisted living facilities. Visit www.simplyrehab.com.
properly, they can set the stage for atherosclerosis and lead
to major cardiovascular health problems.
In the study, 49% of patients whose EndoPAT tests
indicated poor endothelial function had a cardiac event Connextions Taps Auerbach as President
during the seven-year study period. Researchers at Orlando’s Connextions Inc. has named Steven G.
Rochester, MN-based Mayo and at Tufts-New England Auerbach president. He’ll take on overall management --
Medical Center, Boston, used the device to test 270 patients including strategy development, client services, operations
between the ages of 42 and 66 and followed their progress and sales -- for the company’s sites in Florida, North
from August 1999 to August 2007. Some of their risk factors Carolina and Indiana. He was most recently executive vice
were high blood pressure, high cholesterol, obesity and a president of provider/member operations at UnitedHealth
family history of heart disease. The test can be used in an Group (NYSE:UNH). Connextions is part of the New
individualized medicine model of risk assessment of Mountain Partners II LP portfolio. Visit
patients. Visit www.mayo.edu. www.connextions.com.
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Catching Up With….Rong Yi PhD
An industry leader expands the reach of predictive modeling across the globe
Rong Yi PhD, vice president of consulting at Verisk Health Inc., Boston, has probably done as
much as anyone in the business to expand the reach of predictive modeling in healthcare beyond
the borders of the United States and onto the shores of foreign countries throughout the world.
She’s leading a large project with the Health Authority of Abu Dhabi, for example, to develop
health risk assessment tools and predictive models that use lifestyle and medical claims to predict
disease progression and life expectancy.
She already led adoption of US-devised risk adjustment models for the German market, including its migration to
ICD-10 coding, and helped train German health services researchers to use the newly developed models. She also
led research projects for the Kassenärtzliche BundesVereinigung (Physicians Union of Germany) to predict physician
activity levels, risk-adjustment payments and payments by specialty. And she’s working with care management
companies, private insurers and consultants in Germany and the United Kingdom to expand the use of DxCG models
in care management, physician payment, revenue forecasting and other areas. Yi has also presented in China,
opening new markets for DxCG there and in Taiwan, and will return to her native country soon to speak on risk
adjustment and predictive modeling at the International Health Economics Association’s 7th Annual Congress in
Beijing in July.
In her current post at Verisk, she leads the firm’s analytic consulting practice and works with domestic and
international clients to solve business problems that are not typically addressed within standard product offerings. Yi’s
been responsible for the research and development of Verisk/DxCG’s risk adjustment and predictive models for
healthcare, worker’s compensation and disability, and, clearly, for its international healthcare markets. Before that,
she served as a senior scientist at Verisk and as the firm’s analytic services principal. Prior to joining Verisk in 2001,
she was a research assistant and a research fellow at Boston University, where she was also part of the adjunct
faculty at the School of Management and the College of Arts and Sciences.
Rong Yi PhD
• PhD in economics, Boston University, 2001
• BS in economics, Peking University, Beijing, 1995
• In the commercial health insurance space:
--Led the patient-centered medical home pilot project for a physician-owned health plan
--Led three biannual releases of DCG/HCC and RxGroup models
--Responsible for developing the majority of the predictive models in DxCG’s current software releases
--Designed and executed a risk-adjustment methodology for Episode Treatment Groups and Medical Episode Groups
--Developed PM for a large health plan and its contracting physician groups for pay-for-performance programs
--Designed a risk-adjustment study for a healthcare purchaser consortium to compare hospital efficiency
• In the Medicare, Medicaid and uninsured space:
--Led risk-adjustment model selection & implementation project for Massachusetts Health Insurance Connector Authority
--Working on predictive analytics for the Medicare Advantage and Part D markets
--Led and designed research to incorporate DxCG’s relative risk scores into the Medical Expenditure Panel Surveys data
--Led and executed the research to estimate disease burden and healthcare needs for the uninsured using PM
• And in the workers’ compensation and disability management space:
--Developed predictive models and case identification tools for a leading workers’ compensation insurer to prospectively
identify and manage slowly emerging high-cost claims based on initial medical claims following injury
• Recent conference presentations include:
--“Risk Adjustment and Predictive Modeling,” at the 5th Annual World Health Care Congress – Europe, in Brussels
--“Risk Adjustment and Predictive Modeling for Medicaid,” at the 2008 National Predictive Modeling Summit
--“Predictive Modeling Research Methodologies and Applications for Workers’ Compensation and Bodily Injuries,” at a
2008 Insurance Services Office User Conference
--“Understanding the Healthcare Needs for the Under- and Uninsured Population,” at the 2007 National Congress of the
Under and Uninsured
--“Understanding Geographic Variation in the Privately Insured Population in the United States,” at the International Health
Economic Association 2005 Research Meeting, in Barcelona
Predictive Modeling News: What path did you take to your present position, starting right out of college? Was it the
career path you envisioned when you started?
Rong Yi PhD: While studying for my PhD in economics at Boston University, I had to find a way to finance my own
education, so I taught various courses, including finance, financial markets and macro-economics. One of my
advisors was DxCG co-founder Randy Ellis, one of the best-known health economists in the world, who led me to that
company, which became my first full-time job
continued on page 11
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