Special Lecture - Adam W. Culbertson - An overview of the Patient Demographic Attributes Available for Patient Data Matching
Electronic health records (EHRs) offer the promise of improved healthcare coordination, lower cost and improved patient safety. Over $35 billion dollars have been spent to incentivize the adoption of EHRs. To achieve these benefits providers need to be able to share patient health information. To share that information, systems must be able to match patient data from disparate health data providers. Currently, mistakes in patient matching are a substantial contributor to adverse medical events. The cost to correct mismatched patient records can be as high as $100 dollars per record. More important than monetary costs are the potential cost in human lives and subsequent legal cost due if a patient receives the wrong treatment. Two key factors affect the ability to link medical records: 1) the data available for matching, 2) algorithms and method to link this data. We will review of study of the different data elements available for patient matching using a national wide study. In additional look at work that is underway to incentive testing of patient matching algorithms.
Adam W. Culbertson is currently the HIMSS Innovator-In-Residence at the Office of the National Coordinator (ONC) and HHS Idea Lab with a focus on patient matching. He is responsible for the study of innovative solutions to patient matching. Current projects include a nationwide attribute study to determine which attributes are available for matching. The development of a reference data set for patient matching algorithms. Additionally has worked on the patient matching challenge currently underway. He has advocated the moto “If you don’t measure it you can’t improve it”.
Mr. Culbertson completed a biomedical informatics fellowship at the National Library of Medicine (NLM). While at the NLM he worked on the extraction of adverse drug event information using a modified natural language processing. In addition, he completed a fellowship at the Regenstrief Institute. He has a master’s degree in Health Informatics and a master’s degree in Human Computer-Interaction Design where he developed a prototype for an e-prescribing solution. He also has several publications in medical informatics and personalized medicine.
This will be an outstanding presentation, your attendance is encouraged.