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Readers Write: Harnessing Data to Support Population Health Management and the Evolution of Next-Gen Population Health Management

October 15, 2014 Readers Write No Comments

Harnessing Data to Support Population Health Management and the Evolution of Next-Gen Population Health Management
By Larry Schor

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Accountable healthcare delivery is in the midst of a three-stage evolution as organizations increasingly turn to the promise of health IT and data to improve patient care and the bottom line.

First-generation accountable care is all about meeting process quality measures and closing gaps in care. At this stage, provider compensation is loosely tied to compliance with standards of care and protocols for specific common conditions, such as immunizations or screenings for diabetes and glaucoma. However, during this phase, financial rewards predominantly come in the form of bonuses for achieving quality measures with little or no downside financial risk.

As the industry currently evolves from first-generation toward middle-generation accountable care, new complexities are emerging. As such, healthcare organizations must manage clinical risk and begin assuming limited financial risk for identified patient populations.

Because both upside bonuses and limited downside financial risks exist at this stage, it is imperative that patients are clinically well controlled. Clinical data, therefore, becomes increasingly important for understanding risk. The historic reliance on claims data will no longer suffice. It is at this second stage of maturity that next-gen population health management becomes a critical strategy for managing population health because it effectively blends clinical and financial data.

Once healthcare organizations achieve next-gen population health management, mature accountable care — which is characterized by high-performing networks operating under full global risk arrangements — can be realized. This advanced care delivery model focuses on optimization and lowest total cost of care, achieved through high patient engagement as the result of personalized outreach and full next-gen population health management. The benefits of this stage of maturity will be realized through more comprehensive and precise analytics to personalize patient care, especially for those with chronic conditions.

While national initiatives are encouraging the forward momentum of accountable care, a bird’s eye view of the industry reveals that most healthcare organizations are in the very early stages of this cultural shift. Despite evolving reimbursement models that are gradually incentivizing quality outcomes and efficiency, organizations still must invest in the necessary infrastructure and embrace new workflows.

Electronic health record implementation provides one example. To date, even the most sophisticated EHRs usually are implemented as little more than electronic versions of existing processes and workflows. What is needed instead are more comprehensive and precise analytics to segment patients and personalize patient care.

Traditional analytics match demographic and claims data against quality measures, but engage all patients with similar conditions in the same manner. All patients identified with Type 2 diabetes, for instance, might be offered the same form of educational outreach. While EHRs today offer transactional clinical decision support at the point of care—some even are even adding managed care modules—they lack the capability to support the data-driven workflow of a distributed care coordination team. They are not designed to ensure top-of-license performance by all participants in the cycle of care, whether they are charged with managing a patient’s financial, clinical, or social welfare.

With new analytics, however, healthcare organizations can begin to offer a more tailored approach to care based on reviewing more comprehensive claims, clinical, and psychosocial data. As such, future success with population health management requires a data management infrastructure designed to capture an exploding volume and variety of data in real-time, much of it outside the claims stream.

Going forward, the strongest organizations will be those that most effectively harness, integrate, and analyze multiple types of data to inform the care of patient populations at the point of care. For example, claim clickstream data may reveal what treatments patients were provided in the past, but not necessarily whether they worked. Psychosocial data—such as whether a patient drives or has adequate social support—can have a massive impact on the success or failure of care, but is often embedded within provider documentation. Pharmacy, lab, and real-time clinical biometric data from devices such as wireless glucometers and scales is essential to effective care management.

Simply put, a real-time, 360-degree view of the patient, plan of care, evidence-based guidelines and psychosocial data results in more targeted, effective population health management, which in turn leads to better, more accountable care.

Effectively improving population health and the bottom line will require that data be translated into structured content readily available for analysis. Healthcare organizations today must take advantage of technology that allows storage and maintenance of data at its finest-grain level. It is no longer adequate to extract data, drop it into a data warehouse, and run pre-defined reports. This solution simply isn’t agile enough to answer new questions or handle increasing data volumes.

Instead, data must be conditioned, as data hygiene is extremely important for effectively using data out of the chute. Moreover, natural language processing also is becoming increasingly valuable for extracting actionable data from physician notes.

Cloud-based storage strategies, however, have proven most effective for supporting greater volumes of new data. Cloud environments offer an on-demand infrastructure capable of finding the right signals through the data noise that is expanding as the velocity, volume, and variety of data increases. Overall, healthcare organizations must employ technologies capable of clearly identifying relevant data and revealing that data at the point of care in a way that is quickly and easily consumable by providers.

Information is becoming a driver of consumer and clinical value in healthcare. In the near future, the use of data to enable effective population health management will align healthcare organizations with the cost and care quality goals so vital under accountable care reimbursement models. The most successful healthcare organizations, therefore, will be those that find new ways to use technology to leverage a wide range of patient data to improve both the bottom line and patient care.

Larry Schor is SVP of Medecision.

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