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Achieving the “Holy Grail” of population health management in cancer care

To unlock the true value of population health for cancer care, you need the “Holy Grail” of healthcare: clinical information from EHRs, diagnostics and genomic data from labs and pathology departments, and financial data from the billing side.

Population health management, by definition, is the discipline in healthcare that studies and facilitates care delivery across the general population or a group of individuals. The purpose is to use patient health information that is derived from a variety of health IT systems to understand how specific populations are treated, and then to understand their outcomes based on this treatment. As healthcare shifts to value-based care, the industry is making this concept a reality, triggering the realization that curation and use of data is the key to solving the puzzle – especially when it comes to cancer diagnostics and precision medicine.

However, while traditional methods and approaches for population health management have seen some success managing chronic diseases like diabetes or obesity, the same strategies have often failed when it comes to addressing the challenges associated with cancer. To unlock the true value of population health for cancer care, the following components of data are needed: clinical information from the patient record (EHRs), diagnostics and genomic data from laboratories and pathology departments, and financial data from the billing side. The combination of these sources is the “holy grail” of population health data.

Population health in the era of cancer diagnostics and precision medicine
There are a number of challenges when it comes to achieving this “holy grail” of data. First, cancer as a disease generates large, complex data that is multifaceted. Adding to this complexity is the size and scale of some of the diagnostic results generated as genomic testing becomes more common. The data is also extremely disconnected and siloed, often not residing in a single system like the EHR. These systems usually are not in the same network of care either, so the various sets of data cannot be easily and quickly accessed, making care coordination more difficult. For example, clinical data is usually generated by a health system, but most biomarker or genomic test results are generated from laboratories, which may not be part of that same health system.

Communicating data between these different disciplines is just the tip of the iceberg in terms of care coordination, which represents another main challenge. A cancer diagnosis depends on accurate and timely coordination of care that relies on access to unified information. But care teams consist of many different specialists, ranging from the clinical and lab specialists mentioned earlier, to radiology, pathology and oncology – all of which are usually spread across different areas. It’s clear that current systems are not designed to capture and manage all of these different data types, much less link them to clinical and financial data for the purpose of outcomes analysis. Compounding the issue even further, when patients travel from one health system to another and bring their paper-based medical records, the data silo continues to grow and become even further disconnected.

These complexities are further complicated when you take into account the varying subgroups and disease types of cancers in different populations, and how tracking trends in treatment and costs impact outcomes. Traditional tracking and surveillance through cancer registries, which rely on submission of clinical and diagnostic information, often exchange data on a specific cancer types, but do not include details of cancer subtypes. For example, breast cancer staging and subtyping from molecular testing for patients with hormone receptor negative (HR-) and human epidermal growth factor 2 negative (HER2-) for patients diagnosed with triple negative breast cancer are critically important when coupled with race and ethnicity. Capturing disease details from molecular testing in the cancer registry is constantly evolving and is not currently designed to facilitate the addition of financial information to determine outcomes in different race and ethnicity groups.

Payers: the key to population health goals

To reach this “holy grail” of data and make population health efforts successful, payers will need to take the lead. Payers are transitioning to value-based care reimbursement platforms that focus on the quality of care and patient-centered care models, and therefore can no longer ignore the scale and size of cancer diagnostics. They are in a unique position to be a driver of change because they have the ability to collect clinical, diagnostic and genomic data through the claims process, and integrate the financial information needed to get true quality-reported outcomes. They are the primary source of important information used to identify trends or issues that affect cancer patients and survivors across demographics and geographies.

Payers can take charge when it comes to care coordination, disease management and outreach because they already understand how to derive information on patient outcomes through health management strategies. Those strategies begin with data pathways that help physicians order, treat and care for cancer patients from prevention to end-of-life. By combining these pieces and components and taking a pathway approach, we can ensure high quality cancer care delivery and reduced variability.

Connecting the missing pieces

If payers take the lead to influence the industry in the right direction by providing predictive analysis about the economics of cancer, health systems and patients will benefit through improved screening, access to care, pre- and post-treatment, and follow up. The economies of scale that are realized through chronic disease population health management can be applied to cancer to derive insights that capture a more complete patient story.

Population health management efforts will only be successful if all aspects of a patient’s health data are gathered to provide a clear picture of their history in order to accurately treat them. Understanding and linking critical pieces of patient data from the various systems involved in cancer diagnostics is key to understanding cancer’s impact on public health data and becomes critical to true population health management.

Photo: elenabs, Getty Images

 

 

Patricia Goede is vice president of clinical informatics at XIFIN, where she brings 22 years’ experience developing biomedical imaging informatics solutions and technology to facilitate multi-modality and multispecialty image-based exchange, collaboration and management in distributed environments. Goede founded VisualShare and served as CEO until its acquisition by XIFIN in 2015. Previously, Goede was at the University of Utah where she pioneered a number of image, visualization and collaboration tools. She is the founder of the Electronic Medical Education Resource Group (EMERG), and as its director, established the Utah Center of Excellence for Electronic Medical Education. Goede holds an MS in Computational Visualization and a PhD. in Biomedical Imaging Informatics.

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