Precision Medicine and Care: The Role of Decision Management Technologies Before and After the Medicine
cheryl wilson 270003VHSH email@example.com | | Tags:  healthcare business-rules business-rule-management decision-management decision-support predictive-analytics
0 Comments | 3,377 Visits
As a follow-on to last week’s guest blogger post, Healthier Healthcare Using Operational Decision Management, I wanted to comment on this recent and insightful McKinsey Quarterly interview -- Navigating a changing health care environment: An Interview with Pfizer’s Kristin Peck.
McKinsey’s interview with Pfizer’s Kristin Peck, EVP for worldwide business development and innovation, begins with an inflection point: the role and future of the healthcare industry is changing, catalyzed by economic factors, but also by other societal and technological influences that will most likely result in patients taking more responsibility for their health as healthcare becomes more personalized and decentralized.
When asked about the role of technology in the future of healthcare, Peck discussed the concepts of precision medicine and precision care – “using precision tools to identify the right treatment for the right patient at the right time, in a standardized and consistent way."
Before the medicine. Peck listed some of the precision-medicine tools that could be used before the administration of medicine to make more informed decisions about patient treatments, such as imaging, diagnostics and predictive analytics. Here, Peck is alluding to a medical decision support system that goes way beyond electronic medical records (EMR) systems. It’s one that measures its investment on the quality of decisions made and the subsequent impacts on patient outcomes, not on the amount of data captured and shared.
A key decision-making tool that I would add to her examples above is business rule management (BRM). Unlike predictive analytics, which deals in uncertainty and probabilities of events and conditions occurring in the future, BRM deals in certainty, capturing known policies (e.g., medical best practices) and human (physician) expertise and embedding it in systems that can automate decisions or provide support for human decision makers -- in real time, in a standardized and consistent way. Unlike standalone predictive analytics, a business rule management system is able to decide what actions to take based on known policies and expertise. It can also track what decisions were made and why, and what policies were changed and by whom.
This is not to diminish the importance of predictive analytics, but before you even get to the prediction part, it makes sense to embed known medical best practices and expertise into healthcare systems. For example, business rules can be used to recommend best actions to take if someone is given a known drug with known side effects, presenting with certain known symptoms.
Note: Most built-in rule engines that are sold as part of analytics or business process management products won’t deliver given the variability and complexity of decision making in a medical setting – even simple decisions that are numerous, frequently occurring, or need to be combined in complex ways beg for more capable business rule management. Plus you’ll want a decision automation or support technology that can be easily updated by non-technical staff with built-in governance. You’ll want precise, safe decision making that can scale.
After the medicine. Peck also covered the notion of “empowered health” and the use of precision-care tools that come after the medicine – everything from body sensors to behavior modification tools. Empowered health or self-empowered health is an area where technology is enabling the patient to take more responsibility for their health, whether assisting in lifestyle modifications or monitoring of health signs. Decision management technologies, like business rules, events, and analytics, play a large role here, too. As an example, we recently covered the use of rule-based decision management to deliver personalized, automated feedback based on pre-determined health goals and data captured from a body sensor.
Final thought: Peck mentioned in the interview that there will be significant investments made in healthcare IT worldwide over the next five to seven years. I would imagine (hope) that an appropriate portion of that investment will go towards designing medical decision management systems that take full advantage of the available technological capabilities afforded by business rules, events and analytics, even if it means changing the behavior of current systems, processes and humans.