By Ahmed Ghouri, M.D.
Co-founder and Chief Medical Officer
Anvita Health

The data now available to health plans to curb clinical costs and improve outcomes blurs the line between business intelligence and health intelligence. The effective use of both will be the banner of a successful health plan for years to come, but taking that step to integrating business, marketing and clinical data is a big one.

An industry brief by IDC Health Insights analyst Janice Young this month highlighted four key paradigm shifts driven by new market dynamics in the health care payer world:

The brief, sponsored by Anvita Health, further examines the steps to move a payer organization toward a more actionable model.

Through the previous posts to this forum, industry experts have recognized the obstacles to data collection and integration, and in this final post to the Online Forum for Payers, we’d like to leave you with one suggested path to move forward. The IDC Health Insights Industry Brief may be downloaded here.

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By Bob Dolin, M.D.
President and Chief Medical Officer
Lantana Consulting Group
Chairman of the Board
Health Level 7 International (HL7)

Lord Kelvin said over 100 years ago “If you cannot measure it, you cannot improve it”. Taken one step further, I would assert that “you cannot measure it if you haven’t standardized it”. I’m suggesting that interoperability standards are a prerequisite to measuring care, and that they are therefore a foundational component of Accountable Care Organizations (ACOs), which must demonstrate quality.

It is simply not feasible to collect data across care settings if that data isn’t standardized. The sheer volume and variability in structure and format would be overwhelming. Meaningful Use (MU) Stage 1 focuses on getting standardized data flowing – it sets the bar for an MU-certified electronic health record (EHR). Once EHRs can speak in terms of standardized data, it becomes possible to issue standardized quality criteria and decision support rules. For instance, because an MU-certified EHR can generate a standardized problem list and medication list, that same EHR can respond to criteria, expressed using the same standards, such as “active problem of diabetes mellitus” or “current use of an oral hypoglycemic”. In other words, by standardizing the data, we enable standardized quality criteria, and we enable standardized quality reporting.

This vision of coupling quality reporting and shareable decision support to MU-certified EHR capabilities is far from theoretical. It is the basis for the quality reporting work going on in Health Level Seven (HL7, http://www.hl7.org/), National Quality Forum (NQF), CMS, etc. The quality reporting infrastructure being developed in the United States is based on the construction of quality measures that contain criteria written in a way that MU-certified EHRs can interpret.

But while it is clear that standards are a prerequisite for quality reporting, and therefore a prerequisite to ACOs, it is also clear that there are barriers to standards adoption. In a word, the way to simplify implementation is through incrementalism. Incrementalism is a process whereby we build upon existing clinical workflow, in a way that is minimally disruptive, building towards a long-term vision, but taking only small steps at a time. This philosophy is embodied in the HL7 Clinical Document Architecture (CDA) standard, and in the CDA documents developed in collaboration with the Health Story Project (http://www.healthstory.com/). HL7’s latest simplification strategy, known as greenCDA (http://wiki.hl7.org/index.php?title=GreenCDA_Project), appears to lessen the time required to implement CDA-based standards by a factor of 10.

The United States is moving towards a data-driven healthcare system. ACOs are part of this move, and will rely on interoperability standards to demonstrate improvements in care.

By Robin E. Williams, R.N., Product Manager, Allscripts

Any customer-driven organization will tell you that the more you know about your customers, the more effective you will be at serving their needs. And as we all know, the landscape of health care delivery is evolving to demand customer-driven, value-based business practices, from payers and providers alike.

Effective use of data is the secret weapon in successfully making that important evolutionary stride.

Don’t wait until you have an analytics solution or more data, look at the data you have. Most organizations are a long way from having access to everything they want, and my advice is to use what data you have. You probably have more data than you realize. You can always fold other data sources in as your analytics program grows.

The effective use of data can help you better understand where you spend your money and your time. The data will help you to better target patients that fall into either category. Also, invest time in thinking about the workflows and processes in your organization that touch the patient. These are critical aspects to consider when designing the patient engagement campaign, whether it’s a call, a letter, an email, or a combination of these and other outreach methods.

In addition to manufactured patient touchpoints like a letter, the member engagement should happen at any natural patient touchpoint, such as when the patient schedules a visit. Is there opportunity at these touch points to evaluate and assess their general health and well-being? Is there opportunity to get more out of the appointment, by better pre-appointment preparation? Is there opportunity to ensure your patients are receiving the appropriate preventive services?

The retrieval of the patient’s record can trigger real-time analysis to identify any current or prospective gaps in care that can be addressed there on the spot. In the scenario outlined above, fairly basic analytics tools can assess your patients’ medical profiles to identify those who would fall into a category eligible for preventive outreach. More sophisticated analytics tools can extract from your data which previous treatments worked best for the patient, and whether the patient exhibited compliance. But even without sophisticated analytic tools you can understand your patient population, your business practices and make adjustments.

These two general concepts – member assessment and engagement – will be key factors to success in the world of accountable care organizations.

Assessment and engagement become even more important when managing high-cost conditions that entail frequent encounters with health care providers. Identifying at-risk members and engaging them to encourage adherence to treatment is essential to managing risk.

To this end, assessment and engagement are steps that will have to be stitched into everyday workflow in order to accomplish the objectives of an ACO’s mission of keeping people well. While it’s not an insignificant task to alter a caregiver’s workflow, it’s necessary to minimize the burden on the physician whose plate is already full.

While assessment and engagement are not new concepts, together they will drive down risk, and improve quality outcomes in an era that demands better return on health care investments.

By Darren Schulte, M.D., Vice President Clinical Strategy, Anvita Health

As a health care payer, you know that health care reform is driving a more consumer-centric business model. Consumers will have more health insurance options and can more easily switch plans if they find a lower cost, higher quality alternative that meets their current needs.

Consumers will likely demand a greater level of service from health plans and expect a tailored approach for such activities as finding a network provider; comparing treatment options; or searching for information about a disease or condition.  The forward-looking health plan will offer an integrated customer approach with timely, accurate, and personalized support to help members navigate the complex health care arena and achieve their desired health related goals.

Currently, most health plans operate on older architectural models with disparate platforms and data silos created to serve distinct business functions (e.g. claims adjudication; medical management; member enrollment and customer support; network and contract management). A consumer-focused model will place new demands upon payer information technology, such as:

• Consolidation of all member information in a highly scalable and configurable platform to establish one view of each member;
• Intelligent analysis of the integrated dataset to provide valuable insights when required;
• Distribution of insights in an efficient and targeted manner to customer facing applications, programs, and services throughout the organization.

One approach for enabling an integrated customer experience is having a single analytics platform that can truly understand a spectrum of available information, including administrative, clinical, financial, lifestyle, and marketing data, to enable a member-center approach to plan design and administration.

Imagine the following scenario, as enabled by that single, united analytics platform:

1.    A member calls a health plan to find out about primary care doctors in network.

2.    The customer service representative (CSR) retrieves with a few keystrokes a complete view of the member along with useful suggestions and offers.

3.    In addition to locating high quality rated physicians in the member’s neighborhood, the CSR provides lower cost hypertension medication options for the member to consider with her provider to ensure adherence, and offers information about online health coaching for diabetes (current lab tests suggest that the member’s diabetes is in poor control);

4.    The CSR also offers the member coupons for free diabetes test strips redeemable at the local pharmacy where she regularly shops, as identified by marketing data analysis.

This type of member-centric interaction is only made possible with an understanding and timely analysis of medical and prescription claims data; lab results; formulary and benefit design; marketing data; and program design (incentives) at an individual level.  With an intelligent analytics solution, health plans can provide differentiated offerings and ensure consistent and reliable service at all points of customer interaction, to facilitate membership growth and retention.

Posted by Jennifer Horowitz, Senior Director of Research, HIMSS Analytics

As part of the month-long forum that spotlights how clinical decision support tools can help support improved outcomes while minimizing clinical costs, this post focuses on research conducted in January 2011 by HIMSS Analytics to identify how both providers and payers were using and analyzing data to obtain clinical insight.

Spurred by Title XIII of the American Recovery and Reinvestment Act (ARRA), adoption of EMR technologies will continue to expand in the future, increasing the volume of patient data that is captured in an electronic format and available for analysis.  While ARRA incents hospitals for the adoption of new technology, important transformations are also occurring in the healthcare payer space.

These transformations can best be summarized by the fact that a major shift has taken place in the critical role of payers in the overall healthcare system.  This shift is from the payer playing predominately the role of “transaction manager” to one of becoming a true healthcare “informediary” for the purpose of partnering in care delivery to improve health outcomes.  Below are three ways that the data collected by payers can play a role in advancing improved healthcare at reduced costs.

1.     Data sharing: Information from payers can complement the patient’s overall medical record, providing critical pieces of the puzzle.  For instance, a claim is triggered when a patient fills a prescription or completes a test ordered by a physician.  While not fool-proof, this claims data helps providers understand if patients are complying with orders or getting services by another provider. HIEs are an excellent example of a way in which payers are sharing information to enhance patient care.  Another way in which payers are effectively sharing data is by populating personal health records (PHRs) with the myriad of data they have from healthcare providers.

2.     Data warehouses: Many payer organizations are also sophisticated users of data warehouses.  However, the data is most often used to provide retrospective views of data.  For instance, payers are able to use the data to identify trends of care use, which will allow them to establish protocols not only about future patterns of care and wellness programs, but will also help them to uncover instances of fraud and waste of healthcare services by members.

3.     Population health: The data collected by payers spans their entire membership.  This means that they are able to view trends that might take place across their service area, which could cover entire metropolitan areas, or even states - regardless of where care is provided. This provides a powerful source of data that can ultimately be used to formulate where additional programs and services (such as wellness programs) might be effective in ultimately reducing the number of encounters that a member requires with the healthcare provider community.

The future success of controlling costs and improving care will depend on the collaboration of a wide variety of individuals and organizations, including both providers and payers.  This is particularly important as Accountable Care Organizations (ACOs) are further considered as models of the future.

For more information about payer use of clinical analytics, download the Feb. 21, 2011 HIMSS Analytics/Anvita Health Whitepaper, Clinical Analytics in the World of Meaningful Use http://surveys.himss.org/checkbox/Survey.aspx?surveyid=5436.

By: John Moore, Managing Partner, Chilmark Research

The healthcare industry is in a state of major transition. The unsustainable, compounding rate of growth in healthcare costs is forcing a reassessment, and ultimately realignment, of today’s payment models, moving from a fee for service model to bundled payments and risk sharing contracts. Most recently, providers and health insurers have even begun exploring opportunities for collaboration under new Accountable Care Organization (ACO) models.

This transition, however, is creating major challenges as well. Chief among them is quickly and accurately identifying patients and populations at risk and initiating targeted interventions to minimize such risk.

Health insurers are, by and large, pretty savvy in managing population risk among their members to lower medical loss ratios (MLRs). Managing risk, via actuarial risk models, is a core competency of virtually any insurance company, as it is core to their operations and ultimately their profitability. But health insurers’ success at managing risk at the individual member level has not been as successful, and managing that risk in near real-time is challenging for most insurers today. Insurers’ data sets are primarily derived from billing data submitted by providers and are thereby historical in nature. Billing data is also based on specific billing codes (i.e., ICD-9) and not nearly as insightful as the clinical data contained within a physician’s Electronic Health Record (EHR) system. In many ways, insurers have grown accustomed to, and comfortable with, a population-based perspective as opposed to an individual one.

Conversely, healthcare delivery organizations have traditionally done a poor job at managing risk at a population level and a better job at managing individual patient risk.  Increasingly, these organizations are overlaying sophisticated data analytic tools on top of their clinical datasets to accelerate the identification of patient risk and initiate targeted intervention in an acute care setting. Where this model falls apart though is that once the patient is discharged and moves into an ambulatory setting, managing patient risk becomes extremely challenging due to a lack of systems in place that support health information exchange to better manage transitions in care. This may be an area where health insurers could facilitate the care transition process at the individual patient/member level. Doing so would provide a significant benefit to insurers as complications and gaps in care at this stage - particularly those that result in readmission - can be extremely expensive. For these reasons, it behooves payers to become more skilled at managing individual risk.

As in any business relationship where one pays and the other bills, there is a tension between health insurers and providers. Providers seek to deliver the best care for their patients, while insurers seek the best value of care for their members. This is an obvious and logical outcome of their business relationship. But if the nation wishes to truly reign in the spiraling costs of care, a new relationship needs to develop. We need to look beyond the obvious to how the best value care can be delivered at both the individual and population level, looking beyond distinct silos of data residing within healthcare organizations and health insurers to seek out synergies that lead to better management of patients at risk in acute and ambulatory settings as well as the broader population. This will become even more critical as another 30 million individuals are added to the national insurance pool as an outcome of recently passed healthcare reform legislation. Health insurers and healthcare organizations alike now need to manage risk and view their data both on the broad population and individual levels. It may indeed be time to create true partnerships between these often competing interests if we intend to bend the cost curve of health to a more sustainable trajectory.