Precision medicine, defined as a new model of patient-powered research that will give clinicians the ability to select the best treatment for an individual patient, holds the key that will allow health IT to merge advances in genomics research with new methods for managing and analyzing large data sets. This will accelerate research and biomedical discoveries. However, clinical improvements are often designed to reduce variation. So, how do systems balance tailoring medicine to each patient with standardizing care? The answer is precise registries. For example, using registries that can account for the most accurate, specific patients and disease, clinicians can use gene variant knowledge bases to provide personalized care.
Learn more about David Crockett
David K. Crockett, Ph.D. is the Senior Director of Research and Predictive Analytics. He brings nearly 20 years of translational research experience in pathology, laboratory and clinical diagnostics. His recent work includes patents in computer prediction models for phenotype effect of uncertain gene variants. Dr. Crockett has published more than 50 peer-reviewed journal articles in areas such as bioinformatics, biomarker discovery, immunology, molecular oncology, genomics and proteomics. He holds a BA in molecular biology from Brigham Young University, and a Ph.D. in biomedical informatics from the University of Utah, recognized as one of the top training programs for informatics in the world. Dr. Crockett builds on Health Catalyst’s ability to predict patient health outcomes and enable the next level of prescriptive analytics – the science of determining the most effective interventions to maintain health.
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Those in Big Data and Healthcare Analytics circles will seldom hear the phrase “less is more.” In a clinical setting however, there is an important lesson to learn in regards to the effective execution of predictive analytics. We should not confuse more data with more insight. More data is simply more—as in more tables, more lists, more replicates, more clinics, more controls, more rows, tables of tables and lists of lists, etc. You get the idea. In short, for predictive analytics to be effective in a clinical venue, a specific focus will always trump global utility.
Precision medicine processes, while involving genomics, are not confined to working with data about an individual’s genes, environment, and lifestyle. Precision medicine also means putting patients on the right path of care, taking into consideration other individual tolerances, such as participation and cost. Precision medicine processes incorporate data beyond the individual, pulling in socio-economic data, as well as relevant internal and external data, to create an entire patient data ecosystem. With reusable data modules, this information is processed within a closed-loop analytics framework to facilitate clinical decision making at the point of care. This optimizes clinical workflow, thus leading to more precise medicine.
We developed a predictive analytics framework for patient care based upon concepts from airline operations. Using the idea of an aircraft turnaround time where the airline wants to put the aircraft back into operation as soon as possible, we’ve created a way to help patients headed toward poor outcomes, along with their providers, “turnaround” and get the best possible, most cost-effective outcome. For example, in a diabetes patient, we might use variables such as: age, alcohol use, annual eye/foot exam, BMI, etc. to look for patterns that might influence two outcomes: 1) Diabetic control and 2) The absence of progression toward diabetic complications. The notion of our Patient Flight Path is useful at both the conceptual level, as well as the predictive algorithm implementation level.
While many organizations use patient registries from EMRs to determine their patient population, there is a better way. Using GIS location technology, a health system can identify its care population based on geography and drive times. Health Catalyst uses Dartmouth Atlas hospital referral regions, a hierarchy of facility levels with appropriate drive time isochrones, and medical specialties-based central place theory to develop a more comprehensive view of a health system’s minimum bounding geometry. Using this method, ACOs derive a better understanding of their enrolled patients and eligible payer groups resulting a better basis for strategy and decision making.
As far back as the 1840s, clinicians have been using maps to inform them about population health trends. Today, the geo-analytics industry is well-developed in almost every application, with the exception of healthcare and medicine. There is potential to use mapping technologies to show patient disease burden in geographic form, map locations of health care facilities, and a plethora of accountable care population health initiatives would benefit from geo-analysis. Health Catalyst is working to integrate inputs into analysis like maps that can show geographic care boundaries, population health demographics, and more.
This is the complete 4-part series demonstrating real-world examples of the power of data mining in healthcare. Effective data mining requires a three-system approach: the analytics system (including an EDW), the best practice system (and systematically applying evidence-based best practices to care delivery), and the adoption system (driving change management throughout the organization and implementing a dedicated team structure). Here, we also show organizations with successful data-mining-application in critical areas such as: tracking fee-for-service and value-based payer contracts, population health management initiatives involving primary care reporting, and reducing hospital readmissions. Having the data and tools to use data mining and predict trends is giving these health systems a big advantage.
Interest in predictive modeling is part of a larger trend to employ business and clinical intelligence applications in healthcare. Until recently, organizations that had the ability to mine and analyze data were mostly conducting retrospective analyses. Using tools available today, organizations with the right technical infrastructure, including a data warehouse, can link predictions to specific clinical priorities, set up new workflows, apply analytics to emergency departments and to slowly changing clinical situations and more.
Predictive analytics alone cannot offer meaning without context, especially in health care. In order to be successful, prediction tools should be content-drive and clinical-driven. Prescriptive analytics can improve health care better than simple predictions can. Analytics should be used with clinical leaders that have the willingness to act on appropriate intervention measure. This “in context” prediction should include not only the evidence, but also the interpretation and recommended actions for each predicted category or outcome. An underlying data warehouse platform is key to gathering rich data sets necessary for training and implementing predictors.
Predictive analytics is quite a popular current topic. Unfortunately, there are many potential side tracks or pit falls for those that do not approach this carefully. Fortunately for healthcare, there are numerous existing models from other industries that are very efficient at risk stratification in the realm of population management. David Crocket, PhD shares 4 key pitfalls to avoid for those beginning predictive analytics. These include 1) confusing data with insight, 2) confusing insight with value, 3) overestimating the ability to interpret the data, and 4) underestimating the challenge of implementation.
In healthcare, popular buzzwords and hot topics always come and go. Technically sexy topics such as big data, bioinformatics, predictive analytics or genomic medicine are tossed in and about sales calls, funding proposals, journal articles and blogs for a few years and then folks move on to the next big thing. The buzzword fever around predictive analytics will likely continue to rise and fall. Unfortunately, lacking the proper infrastructure, staffing and resource to act when something is predicted with high certainty to happen, we fall short of the full potential of harnessing historic trends and patterns in patient data. In other words, without the willpower for clinical intervention, any predictor – no matter how good – is not fully utilized.