As the U.S. healthcare industry continues to evolve and undergo massive structural changes, the outcome that consumers, providers, and payers are looking to see is higher quality care at a lower cost. In order to achieve that, providers are beginning to leverage analytics based on clinical and administrative data to more effectively predict the health outcomes of their patient population, measure health trends, and establish meaningful correlations to help make more informed healthcare decisions. By Keith Blankenship, VP, Technical Solutions, Lumeris
By Keith Blankenship, VP, Technical Solutions, Lumeris
As the U.S. healthcare industry continues to evolve and undergo massive structural changes, the outcome that consumers, providers, and payers are looking to see is higher quality care at a lower cost. In order to achieve that, providers are beginning to leverage analytics based on clinical and administrative data to more effectively predict the health outcomes of their patient population, measure health trends, and establish meaningful correlations to help make more informed healthcare decisions.
By having a complete view of a patient’s health status – informed by claims, lab results, EMR data, and pharmacy records – providers can help to improve the quality of care they are providing while cutting costs through the elimination of unnecessary tests and treatments as well as utilization of more appropriate care settings (i.e., outpatient surgery centers).
While the healthcare industry is heading in the right direction, the challenge of collecting the right data still remains. Collecting data simply for the sake of collecting data will not have a measureable impact on quality, cost, and utilization. For payers, managing quality and reducing costs is about collecting the right data, then aggregating, enhancing it through analytics, and, finally presenting it in an actionable format that can impact healthcare decisions. For providers, payers need to provide powerful incentives and tools to turn the information into accurate insights as part of the physician’s and care team’s workflow.
Establish correlations that drive quality care at lower costs
Using a population’s health data can be beneficial for healthcare decisions at the patient level. Patient-level data can be useful for predictive opportunities at the population level. By combining biometric information – such as a patient’s BMI, blood pressure, LDL cholesterol levels, and blood sugars – with clinical data that has been analyzed at the population level, providers and care teams can deliver improved care for individual patients which in turn leads to better care overall. Furthermore, these types of analytics help health plans identify trends – such as high-utilizing patients – and correlate that data back to a specific condition, as shown in the following example.
If a health plan notices an increase in asthmatic ER visits, they can then use that information to implement a care-management plan. Based on this plan, physicians and their care teams can complete assessments and care plans for each patient, educate the patients about their chronic conditions, stress the need to adhere to symptom-response plans, and schedule regular checkups. The result: asthmatic ER utilization will decrease, and associated hospital admissions and readmissions will decrease as well. Because the health plan identified a trend in their population, they can work with physicians to provide high-quality care for their individual asthmatic patients.
The same concept is true when discussing cost and claims utilization. Without financial information, a health plan cannot reduce costs. When coupling claims data with EMR data, health plans and providers can achieve a more complete view of the patient’s health status and reduce cost by focusing on the essential gaps in care that need to be closed. Having a view of claims information will give providers a view of services that are being inappropriately administered, and then point patients to better settings that can provide the appropriate care at a reduced cost (i.e., an urgent-care center instead of the ER).
Identify and collect the right data, now what?
Administrators and clinicians are currently collecting and entering the right type of data. Biometric, social history, family history, diagnostic, procedure, and medication data at a patient level are collected both in EMR’s – and to some extent through medical and pharmacy claims. The challenge lies in integrating all of the available data and having the right tools to turn that data into useable information to enhance critical care decisions. The key is not just gathering data, but extracting it and using it in a physician’s daily workflow.
At this point, data is accessible only to the business or IT analyst, who may not understand what can be done with the data to improve care and reduce cost. The analyst may decide what he or she thinks is important enough to send to the health plan or system executive without any insight or investigation into what that data can mean. To add to the problem, physicians and payers are still acting as adversaries by having a separate set of incentives that do not align with one another and therefore work as separate entities. In this scenario, the analyst, the health plan, and the provider are working in a “siloed” manner, independently of each other without a real understanding of the benefits that the data and analytics can provide (and often looking at different data sets with different analytics, leading to different results). In value-based care, none of these parties can be successful working independently without the other’s insights.
To create better health outcomes at reduced costs to health plans, there needs to be collaboration and access to the correct data, advanced analytics, and real-time reports – provided in an integrated platform – that can be used and understood by anyone in the healthcare system. For example, in an ideal model, once a decision-maker receives a report about a physician’s generic dispense rate, they can send that exact report on to the physician. When the physician logs into the platform the next day, they see that their generic dispense rate is low, and he or she can begin correcting that in the daily workflow.
Making more meaningful, data-driven decisions
Though data-analytics tools have proven to enhance the quality of care delivery, it does no good if healthcare providers are not taking advantage of them. There needs to be some incentive to use a platform and technology-enabled tools, beginning with the alignment of motivations across all of the players in the entire continuum of care. A further objective is to look at the information and identify where opportunities lie to enhance provider performance.
Payers, health systems, and providers should collaborate, define metrics, and focus on contracts that incentivize all members of the system to improve the quality of healthcare and reduce costs. Successful clinical data analytics will identify key data as well as make that data understandable and useful to those that it impacts most. When predefined metrics are met, the providers and care teams involved should be rewarded for their efforts, thus increasing their satisfaction with the services they provide. And, once this happens, healthcare organizations can achieve the Triple Aim Plus One: better health outcomes, lower costs, and improved patient plus physician satisfaction.
About the author
Keith Blankenship is a software development and technology executive with more than 24 years of development and engineering experience. In his role at Lumeris, he is responsible for the development of various technology solutions that improve healthcare coordination, collaboration, and overall accountability. Prior to joining Lumeris, Mr. Blankenship was Director of Application Development at Coventry Health Care and held various management positions at Blue Cross and Blue Shield of Missouri.
