This article is based on a Healthcare Analytics Summit (HAS 20 Virtual) breakout presentation by Keegan Bailey, MS, Senior Leader, Acuitas Health, and Andy Choens, MS, Leader and Architect for Data Science and Engineering, Acuitas Health, titled, “Advanced Analytics for Medical Practices: Value-Based Care in the New Normal.”
Challenges for healthcare providers in today’s competitive market include stagnant reimbursement rates for value-based care (VBC), high overhead, stiff competition from healthcare conglomerates, and as of 2020, COVID-19. Increasing demands to deliver better care with fewer resources, combined with the pandemic, have heightened the pressure for all organizations, especially for independent medical and physician practices with fixed resources and less financial flexibility than larger organizations.
While some smaller providers are joining large health systems, those aiming to stay independent must strategize for a viable financial future. Many independent practices struggle to survive because they rely on manual processes or technology that fails to maximize data. With technology that surfaces meaningful information based on analytics insight, providers have the most relevant information when it matters—from managing the practice to the point of care. Not only do analytics for medical practices drive better patient care, but also better financial and operational decisions by automating practice management, regulatory requirements, and staffing decisions so that providers can spend more time with patients.
Delivering quality care to patients is critical for independent practices because it is the primary source of income. Without patients, these practices can eliminate waste and improve resource utilization but cannot generate the new revenue that comes from patient visits.
The burdens facing physician practices include spending $15.4 billion annually to report over 2,000 quality measures to CMS, a costly requirement to qualify for reimbursements around VBC that takes providers away from patient care. Medical practices also face new pandemic-related challenges, such as added financial strain from paused elective procedures, delayed primary care visits, and transitioning from in-person to virtual care. Typical care coordination between medical practices and health systems—critical for communication about patients who receive care at multiple locations—is also more difficult due to the lack of interaction between providers and health systems amid the COVID-19 response.
While these challenges are ubiquitous to health systems of all shapes and sizes, medical practices, in particular, experience more pressure compared to large health systems because they have fewer resources (e.g., staff, supplies, and revenue) to share the burden of an unexpected crisis, like COVID-19. Limited financial, clinical, and operational resources make it critical for medical practices to rely on analytics to maximize every asset, supply, and material they have.
Despite new and existing pressures, medical practices can leverage four analytics-based strategies to navigate growing healthcare challenges, swiftly respond to market changes like COVID-19, get back on the road to recovery, and plan for a sustainable future:
Analytics for medical practices only offer peak value if they are based on broad and varied data sources. A singular data set can be useful but has limited value because it lacks context from other data sets. Therefore, medical practices need access to a variety of data sets from diverse sources to derive analytics insight about their operational, financial, and clinical well-being.
When medical practices have access to expansive data sets from a robust data infrastructure (e.g., the Health Catalyst Data Operating System (DOS™)), they can leverage analytics insight as often as they need to review information about patients, interventions, or the practice’s operational status. By drawing on the latest analytics insight, health systems don’t waste time or resources on a substandard solution because the analytics will reveal any wrong direction in near real-time.
Constant guidance from comprehensive analytics allows for a flexible problem-solving approach and better understand challenges along the way. This flexibility in an ever-changing healthcare market leads to optimum solutions. For example, EHRs fail to effectively synthesize information, hiding key data points in hard-to-find places so that clinicians have to click multiple times to find one piece of information. In such cases, analytics based on comprehensive data sets would reveal that providers waste time clicking in the EHR searching for information. With this insight, medical practices could create an automated solution to surface insights in the EHR and save provider time and avoid burnout, leading to better patient care, as the clinician could easily view a patient’s comprehensive history in one place before making a healthcare decision.
Along with a diverse approach to data sources, medical practices should also take a diverse approach to data governance. Historically, a data analyst has focused on technical elements, including algorithms, predictive models, and plugging numbers into spreadsheets. In contrast, the subject matter expert (SME) only focuses on delivering in-depth subject matter content. However, this approach doesn’t advance analytics for medical practices beyond the surface level because neither the analyst nor the SME understands the other’s role, keeping their contribution limited to their subject areas and creating hard-to-break information silos.
In a multidisciplinary analytics setting, data analysts learn the subject matter, and SMEs understand data’s technical aspects. This cross pollination across different areas of expertise allows a medical practice to approach problem solving with analytics from all critical viewpoints. For example, a data analyst might interpret or value a specific data set differently than a SME. With data analysts, SMEs, and other contributors working together to govern data, a medical practice ensures meaningful solutions across the organization.
Translating data into analytics insights is the key to deriving value from data and ensuring it can drive real-world improvement. The translation process involves members of specific teams (e.g., business management, data science, and administration) working together to interpret the problem and proposed solution for team members less versed in advanced healthcare analytics. Historically, non-clinical workers at an independent practice might focus on tasks like accounting or quality metrics data entry to track physician performance. Now, every team member who interacts with data should act as a strategic partner in driving organizational change, from non-clinical to clinical personnel.
Additionally, including the data governance team early in the data process, before the team commits to a solution and course of action, allows for additional questions and contributions that can lead to further analytics clarification and insight the core team might have missed. Regardless of the problem, medical practices should include different team members in data analysis to ensure the data translates to information that can drive real-world improvement at a medical practice.
The rapid onset of COVID-19 forced many medical practices to quickly modify their operations to address pandemic challenges. During the acute phases of the COVID-19 outbreak, medical practices have gone (in just a few days) from delivering no or few telemedicine visits to thousands. In the past, many health systems delayed implementing or scaling telemedicine, but with suspended in-person visits, medical practices have had to turn to virtual care.
Robust analytics infrastructures with access to aggregated data from multiple sources have helped medical practices visualize COVID-19-driven changes on operational and financial performance and how to best respond. For example, if a medical practice has to deliver 100 telemedicine visits to meet a specific financial goal, leaders will know how many virtual visits to aim for weekly.
Without access to accurate data, medical practices must guess where they should expend their precious resources, rather than knowing for certain. If larger organizations misuse resources, they generally have a pool of back-ups. But with a small, if any, safety net, medical practices can’t afford to use any resources in a way that isn’t data-informed. Every wasted resource becomes a resource that the medical practice can’t get back and now has to take from another area of the practice.
Analytics insight from comprehensive, diverse data supports an effective response to any demand, including maximizing limited resources or responding to a global pandemic. However, medical practices cannot rely on analytics insight alone to drive change. Analytics for medical practices, coupled with team members who understand data and can translate it into real-world analytics, lead to operational, financial, and clinical long-term viability, critical to surviving in the pandemic and VBC landscape. Access, teamwork, and infrastructure are vital in pushing analytics beyond their basic use, unlocking insights that could make the difference for a medical practice’s survival and empowering them to worry less about their financial future and more about delivering first-class care.
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