Incorporating Evidence-based Decisions to Support Gut Calls in the OR: The Entry of Case-based Reasoning into Healthcare
Guest post by Neal Benedict, CEO, healthcare, Verdande Technology.
With increasing financial pressure on the industry, healthcare is being redefined to focus on quality outcomes at lower costs. Providers in particular need to look to new ways to utilize data to improve outcomes, while taking into account the rapid changes that can occur during a case. No matter how prepared physicians may be before surgery, the situation can shift dramatically on the operating table and physicians need evidence-based support to make the smartest real-time decisions.
While the accumulated experience and skill of physicians allows them to make gut calls based on instinct, there is no substitute for data-backed, evidence-based information to support these calls. Many hospitals and physicians currently do not have the tools or technology to leverage the inordinate amount of data they produce to assist in making decisions in real time.
Hospitals must run just like any other organization, focusing on the business of care. Preventable adverse events cost the healthcare industry $17 billion to $29 billion annually and 50,000 to 100,000 people die each year in the U.S. as a result of medical errors. The industry simply cannot sustain care with these annual loses. The key to preventing these adverse effects, and saving both money and improving patient health, is looking to the past for guidance.
The medical profession has long used case studies as a way to learn from the past. There is no shortage of particularly interesting studies which have provided valuable lessons for future treatment. With situations in the present often being similar to situations in the past, it’s important to learn from these case studies and pull best practices and tactics from surgical procedures and techniques that resulted in positive outcomes in the past for use today.
Realistically, no single person can keep every relevant experience and case in their head, and it can be difficult to recall a specific case during a procedure. This is where Case-based Reasoning (CBR) comes in. A CBR platform can contain a large number of cases in a database. Instead of having a physician actively search for cases with traditional knowledge management techniques, the CBR system uses multiple, heterogeneous data types about the patient to index the past experiences. This allows the system to use real-time data from the patient to continuously search the database for past cases and relevant risk profiles, actively providing the physician with relevant cases with information they need to improve patient outcomes. By providing a realistic assessment of whether a similar scenario is likely to occur in the future on the operating table, CBR empowers physicians with the evidence-based insight and foresight to make better, smarter, faster real-time surgical decisions to ensure positive patient outcomes.
CBR is currently making its way into cardiovascular surgery, thanks to the work of The Methodist Hospital System. Perioperative blood loss is a well-researched problem in open heart surgery, but the majority of the existing studies focus on select factors instead of developing a framework combining static, pre-operative information with real-time, dynamic intra and postoperative data. The Methodist Hospital System cardiology team is working to develop a CBR-driven application to combine historical pre, peri and postoperative patient data, bolstered by the cardiovascular team’s expert knowledge, to guide the design, implementation and testing of the data mining procedures.
By evaluating real-time predictions of possible adverse outcomes, the Methodist team anticipates that the system will permit improved patient care and may represent a new standard in real-time predictive modeling.
With these solutions in the works, one may question how CBR differs from the traditional predictive analytics technologies used by healthcare providers today. While predictive analytics solutions are valuable for analyzing historical data for patterns that can inform future medical decisions, they do not analyze data in real time or serve as decision support tools during a procedure.
Seemingly minor trends and symptoms can turn into major complications during surgery, if not identified and treated in time. CBR helps physicians identify these trends and symptoms early and suggests the best course of action to improve the outcome for the patient. By having accurate, evidence-based information at their fingertips, physicians are assured that if the current patient shows similar trends and symptoms as a past case where complications occurred, they have the necessary background information and best recommendations available in an instant. Better patient outcomes naturally result in shorter stays in the hospital, reduced costs and fewer lawsuits. Taking this into account, CBR is not only helping improve quality for patients, but also benefitting the provider’s bottom line.
The success of hospitals relies on the success of its physicians and patient care. With many healthcare providers struggling financially as they try to support an exemplary quality of care, physicians must look to the past success of their own work and the work of their colleagues and adverse events to enhance future performance. There’s no substitute for the individual skill and instinct developed during a physician’s career, but a decision support solution like CBR can only help improve patient outcomes while reducing costs and enhancing the quality of care.
Neal Benedict, CEO, healthcare at Verdande Technology, has held various senior level sales and marketing roles in the oil and gas industry. He also has held senior leadership roles at Reuters and Intel, being responsible for partner alliances, marketing strategy, sales and strategic planning. He has a BA in political science an M.Ed, as well as an MBA, all from the Pennsylvania State University.
One comment on “Incorporating Evidence-based Decisions to Support Gut Calls in the OR: The Entry of Case-based Reasoning into Healthcare”