Longstanding models of care management and healthcare delivery in America are being reevaluated in a new landscape of economic pressures, unprecedented rates of medical innovation, and consumer demand for the highest quality healthcare.
The continually rising cost of healthcare has necessitated higher premiums and/or shrinking margins for hospital systems and provider groups.
Experimenting with alternative models of care management, medical necessity criteria, provider and hospital network management, and reimbursement is producing new and innovative models of care management and delivery.
Further, advances in standards of care and medical knowledge are challenging healthcare providers and payers to accommodate innovation at equally unprecedented rates.
Systems must incorporate new and, in many cases, expensive clinical interventions while containing costs and improving quality of care. Partly as a reaction to these pressures, the Integrated Delivery Networks (IDN) model has evolved from hospitals and hospital systems.
Though there are multiple definitions of IDNs, for our purposes, IDNs are vertically integrated health services networks that, under a single corporate umbrella or management system, include at least one general acute-care hospital, physicians, pharmacy, and other post-acute services. The most mature of IDNs also may include one or more health plans as well as major academic health centers. Though there are a few national IDNs, most provide services to patients within a fixed geographical region.
Providers’ and systems’ performance is monitored by governmental and healthcare quality agencies to ensure safe, effective, affordable, and accessible healthcare. Performance is monitored through evaluation of empirical metrics, many of which are based on the system’s own transaction and electronic medical record (EMR) data, while other metrics rely on the capture of clinical and survey data that must be incorporated into the system’s care management operations.
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The resulting database of transaction, EMR, clinical, and survey data represent a data source that, if analyzed correctly and thoroughly, could provide powerful and actionable intelligence that would facilitate the development of systems that meet and exceed quality standards, improve outcomes, contain cost, and adapt to the often shifting economic, clinical, and regulatory landscape.
However, analytic resources within most regional IDNs are typically at capacity with routine reporting in support of utilization management, quality measures, and other basic business concerns and government regulatory requirements. Their reporting loads leave little capacity for analysis of data for clinical-program development or evaluation, clinical-model development, or other similar analyses that could facilitate system development.
Such research-focused analytics, sometimes called Health Economic and Outcomes Research (HEOR), provides focused real-world evidence (RWE) that could provide the operational, health economic, and clinical outcomes guidance needed to plan and implement improvements in clinical interventions, care management, disease management, formulary, etc., while ensuring future cost containment.
HEOR Analytics: A Key to Solving Healthcare Delivery Challenges
Health economic and outcomes research as a discipline is a powerful tool that may be used to solve many of the most intransigent challenges within healthcare. However, HEOR research rarely, if ever, generates enough revenue for systems to afford a dedicated research organization or team. In fact, most systems that invest in a full-time research team typically must underwrite anywhere from 20% to 30% of the total annual operating budget. For most systems, this is untenable, even though this intelligence and evidence could continue system evolution at the pace of healthcare advances while containing cost.
One solution embraced by more forward-thinking systems is development of a close working relationship with a trusted HEOR group. Such a relationship, if structured properly, allows the system to reap all the benefits of the experience and expertise of the research group while incurring little of the cost.[1] HEOR research groups routinely develop study protocols, analytic plans, data source and extraction documents, statistical analysis and results tables, reports, manuscripts, conference presentations, etc., focused on evaluating operational, clinical, economic, or other dimensions of healthcare imperatives to system growth and development.
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Why should manufacturers collaborate with IDNs on HEOR projects?
The IDN model possesses key strengths that can address many of the greatest challenges to entities that manage and/or provide care. The strength of the IDN model combined with the data that they possess make them desirable data partners. What follows is a closer look at the rationale for collaborating with IDNs on HEOR projects.
- IDNs house valuable clinical, administrative, and quality data. However, they have little capacity to analyze them outside the daily routine reporting regimen. The challenge is two-fold: creating access to data in responsible ways and translating simple data into intelligence that meaningfully influences decisions around the purchase and use of goods and supplies.
- IDNs will ultimately exert more influence over both payers and providers as the reduction of healthcare costs and improvement in patient outcomes become more entrenched as the overarching measurable goals in healthcare delivery. The confluence of the ACA legislation, belief that IDNs increase quality of care and efficiency, fiscal stability, volume pricing capability, and IDNs’ willingness to exert influence over provider practice patterns, manifests in organizations with a surprising amount of influence over standards of care, pharmacy formulary, reimbursement policy, and contract negotiations.
- Industry and IDNs have much to learn from each other. Market access detailing is helpful as IDN leadership learns how to optimally use new medications and technology. However, detailing returns little knowledge to the manufacturer about IDN structure and process. Similarly, there is little that IDN leadership can learn about process efficiency, clinical-outcomes management, and cost containment from a manufacturer’s detailing on their products. Detailing is not always the best method for increasing trust between organizations, as the two parties’ goals are often diametrically opposed.
Discussions between IDN and manufacturer staff within HEOR collaborations are typically focused on projects rather than products. Learning about the disease state, treatment class, products within the class, and how the IDN manages its patient populations, outcomes, service utilization, and cost of care within the disease state and treatment class is of interest to both the system and manufacturer. Even with projects that focus on a single product, conversation can focus on its qualities in the market rather than its cost, how the system is treating the disease state, and how outcomes can be maximized, all of which can be born out in HEOR study results.
- Collaborative HEOR projects build longstanding collegial relationships. There is no need for business negotiation and the tension it brings to relationships. Rather, the study team that includes IDN and manufacturer leadership, form a collegial bond focused on advancing healthcare through discovery and reporting.
Examples of Manufacturer/IDN Collaboration – Two Different Kinds
The following examples highlight two methods that Health Analytics previously employed to initiate and develop relationships with its IDN partners.
- HEOR Project. An IDN and a manufacturer in the rheumatic disorder space agreed to collaborate on a biologics-focused project. The project included evaluation of specialty pharmacy data linked to patient survey data. The hypothesis was that the dimension measured by the survey influenced adherence and persistence with the biologic, leading to better outcomes and cost containment. Contracting and project launch went smoothly and within 2 months, the protocol had been drafted, data were being extracted from the IDN’s transaction system and within 6 months, preliminary analytics were completed, and results tables were interpreted jointly by the IDN, manufacturer, and HEOR group staff.
- Research Grant. An IDN and manufacturer agreed to collaborate on a diabetes project that required EMR and survey data integration. After several months of negotiation, the parties agreed to structure the project as a grant to the IDN. Ownership issues were obviated as the manufacturer made no claim of ownership. The study was contracted within 9 months and completed within 4 years of project initiation.
Challenges Collaborating with IDNs
Collaboration between manufacturers and IDNs on any project raises regulatory concerns, including ensuring a firewall between the study team and commercial departments within both entities, estimating fair market value, and ownership of confidential information, data, and study results. The concerns that have the greatest potential to disrupt or block a collaborative project are firewall, fair market value, contracting, and intellectual property ownership issues.
- Manufacturers’ interactions with systems are regulated and an exchange of goods and/or services is scrutinized to ensure that there are no illegal incentives or buried gifts. While the firewall within the manufacturer might be adequate, as a healthcare service provider, IDNs rarely have firewalls that are well enough established for this purpose. Most collaborations are abandoned at this stage, as it is far too costly to estimate fair market value of contributions to the collaborative effort by both entities.
- Contracting with IDNs and manufacturers to collaborate on an HEOR project is typically associated with disclosing trade secrets. Even in the presence of a non-disclosure agreement, many manufacturers are reticent to share their secrets. In cases where the trade secret includes cost, health economic projects could be hobbled without accurate cost data, and neither side would be incentivized to disclose the trade secret. For most manufacturers, business concerns trump the goals of any HEOR study, either leading to project cancellation or eviscerating the results.
- IDNs are often born of multiple systems (hospitals, group practices, specialty practices), each of which may have managed their EMR, claims, laboratory, pharmacy, specialty pharmacy and other data on different legacy systems. The result is often lack of interoperability between electronic systems and, without a centralized data warehouse, data extraction and analysis are difficult and sometimes cost prohibitive.
- Finally, ownership is a complicated issue in these collaborations. Each party wants sole ownership of data and study results. In most cases, all collaborators agree that the raw data, whether they are claims, electronic medical records, or other healthcare data that derive from the IDN’s transaction system are owned by the IDN or an organization within it. When, as part of a study, survey data are collected from patients, providers’ collaterals, or any combination of these, data ownership becomes less clear. If the data were collected as part of the clinical process, using the electronic medical record for example, then there is an argument that the IDN owns the data. If collected purely for a manufacturer-sponsored study, however, the manufacturer might claim ownership. Once the data are cleaned, managed, and analyzed, there is the question of who owns the study results. In most cases, collaborations are based on the manufacturer’s paying most of the expense of the study, which facilitates the argument that the manufacturer owns the study results. By contrast, when IDN staff are performing the calculations, they might argue that the IDN owns the results. Ownership is not as clear with study results as with raw data. However, it is essential that ownership be resolved prior to starting the collaboration. When collaboration is working well, the parties might agree to joint ownership.
Role of a Third Party HEOR Group
As an HEOR vendor with experience working with data partners of all sizes, from small group practices to hospital systems, IDNs, and health plans, Health Analytics can help overcome the impediments to collaborative efforts between manufacturers and any data partner. Health Analytics has realized the benefits of working with manufacturers on collaborative HEOR projects with IDNs through multiple projects, some of which are still ongoing. Equally important, Health Analytics was the innovator of many solutions to the challenges of working with IDNs that are still in practice today. Examples of such solutions in successful studies can be furnished upon request. Health Analytics’ role in collaborative HEOR projects with IDNs can be summarized as follows:
1) Contracting intermediary: We hold the contract with the manufacturer sponsor and a data use agreement with the data partner. The IDN and manufacturer need not contract directly, which reduces regulatory and legal concerns for both parties.
2) Communication intermediary: We facilitate communication between the manufacturer and the IDN by keeping it focused on the HEOR project. The relationship between IDNs and manufacturers can be competitive and defensive, but when collaborating on an HEOR project, we facilitate a collegial and collaborative relationship.
3) Science and analytics intermediary: We handle all project data; raw data never pass through Health Analytics to the manufacturer. Our experience with raw data (e.g., claims, EMR, pharmacy, lab, medication therapy management) from many sources means that we can anticipate and avoid the pitfalls that come with working with raw data and transaction systems. As with all our HEOR projects, we manage and analyze the data according to agreed-upon protocols and generate all reports, which can include conference presentations and publications.
Summary
The value in collaborating with IDNs is clear: they are here to stay and are gaining influence over practice and prescribing patterns. The question is no longer whether a manufacturer should collaborate with an IDN on HEOR projects, but which IDNs make the best HEOR partners. As the coordinator and methodology experts for IDN/HEOR collaborations, Health Analytics serves to build strategic partnerships, bridge knowledge gaps, apply results to practice, and advance healthcare.
[1] Systems will always contribute clinical and medical leadership to relevant studies and IT or Informatics staff time to support data needs.