The Use of Analytics In the Business of Medicine

William R. Pupkis, CMPE, Healthcare Consultant
Fee-for-service has been the typical business model for medicine since the beginning of time. This is especially true for orthopaedic medical care in most markets across the country. However, with the passage of the Affordable Care Act, that is slated to change.

A major component of this law is the development of a national quality improvement strategy that includes priorities to improve the delivery of healthcare services via patient health outcomes. There will be initiatives to create processes for the development of quality measures involving input from multiple sources to be used in reporting to, and payment under, federal health programs. The law also requires the development of plans to implement value-based purchasing programs for ambulatory surgical centers, many owned by orthopaedic surgeons.

These measures can push the payment system away from fee-for-service, which is volume-based care, to a value-based care system. No longer will health care be about how many patients you see, how many procedures or tests you order, or how much you charge for these things. Instead, it will be about costs and patient outcomes: quicker recoveries, fewer readmissions, and lower infection rates, to name a few. It will be about value.

If that is to be the future, then medical practices will need to answer questions that until now have not been asked such as, how one’s practice has better patients’ outcomes compared to other practices in the community. Where would you find that information? There is no shortage of data within the practice management and electronic medical records in use today for most practices that will give you the answers.

Analytics is the systematic use of data and related business insights developed through applied analytical disciplines, i.e., statistics that drive fact-based decision making for planning, management, measurement, and learning. Analytics allows you to sort through the volume of data currently available to answer outcome-related questions, i.e., what is your infection rate, how many readmissions did you have, etc.

Analytics may be descriptive, predictive, or prescriptive. Descriptive analytics look at past performance by mining historical data, and finds the reasons behind past successes or failures. Most management reporting, such as finance, uses this type of post-mortem analysis. Predictive analytics is used to help answer questions of what will happen. And, prescriptive analytics goes beyond predicting future outcomes by suggesting actions and showing the implications of each decision option.

Practices have been using baseline transaction monitoring, utilizing basic reporting tools, spreadsheets, and application reporting modules, for years. With the movement to value-based care models, insurance companies and patients will now want additional information about treatment plans and outcomes.

Analytics can be used to manage small details to large processes, which can lead to improved service delivery and operations, by providing a means for measuring and evaluating critical organizational data. Analytics in medicine is a move toward a model that can be used to drive clinical improvements to meet future challenges, such as reducing variation in clinical practice, utilization rates, and helping to eliminate unpleasant surprises.

Forty percent of the medical care data provided in the United States may not add value, based on reports from the Dartmouth Institute for Health Policy and Clinical Practice. A study released by the American Academy of Orthopedic Surgeons (AAOS) in February 2012 found that 96 percent of orthopedic surgeons practice defensive medicine. Findings show that 24 percent of tests being ordered were for defensive reasons and without significant benefit to patients, and 35 percent of specialist referrals by these surgeons were also motivated by defensive medicine. In order for health care to move towards value-based, rather than volume-based, models, physicians need to work together to remove unnecessary steps in the care process.

There is no shortage of data in a medical practice and the sheer volume of data that needs to be analyzed can inhibit the development of meaningful insights. There is a real need to use analytics, integrated from multiple sources, and standardized to better ensure consistent definitions throughout the practice. This should allow practices to modify and manage relationships, as well as motivate and modify behaviors. Practices can set up value-based care teams to review both internal and external benchmark data, thus eliminating unnecessary practice variations by developing evidence-based care paths to improve care coordination for moving patients more easily through the system.

Many medical practices use reporting tools descriptively to understand what has happened in the past, and to categorize historical, structured data. Some organizations focus on data warehousing to create financial and operational dashboards, and clinical data repositories. As these are typically real time databases, data is consolidated from a variety of sources to present a unified view of a single patient. The goal is to allow clinicians to retrieve data for a single patient rather than identify a population of patients with common characteristics, or to facilitate the management of a specific clinical department. This explosion in the amount of structured and unstructured clinical data makes data warehousing essential for turning the stored data into actionable information.

In order to begin establishing your value-based practice model, you must first start with questions, not data. Gain valuable insight into the various problems that need to be solved by asking questions that you would like answered. For example, if a department requests another FTE, bring all the staff involved in that aspect of the practice together and determine each person’s role within that department. This should lead to a better understanding of the organizational information needed to help resolve the problem and the data that can be used to generate that information. A method that I have found most helpful is to use post-it notes, writing down every step in the process, putting all the post-it notes on a wall, then stepping back to determine which steps are absolutely necessary, consolidating where possible to make sure that each step is adding value.

Analytics leaders foster appropriate information sharing through better data management and new approaches. They keep existing capabilities while adding new ones, trying to create analytics initiatives that are scalable and flexible, while not growing too complex or costly. They understand that it all starts with defining issues and desired outcomes. Asking the right questions will illuminate the data that matters, and will bring objectives and targets into better focus.

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This entry was posted on Monday, April 27th, 2015 at 4:04 pm and is filed under Practice Management. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.

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