Primary care providers frequently prescribe antibiotics for conditions such as acute otitis media (AOM), acute bronchitis and possible or suspected pnemonia. In many cases, the use of antibiotics is not supported by the evidence, thereby exacerbating the problem of antibiotic resistance and putting patients at risk of adverse drug events. Read further >
Vice President, Medical Informatics
Wolters Kluwer Health
As the VP of Medical Informatics for Wolters Kluwer Health – Clinical Solutions, Howard focuses on building products that answer clinical questions and integrate knowledge with electronic medical record (EMR) and computerized physician order entry (CPOE) systems. He is also actively involved in standards development as a co-chair of the Health Level Seven (HL7) Clinical Decision Support (CDS) Technical Committee, which develops CDS standards in areas such as Infobuttons, order sets, and decision support services.
Prior to joining Wolters Kluwer Health in 2003, he was CEO of Skolar, Inc., an online provider of clinical information and "in context" continuing medical education (CME) for medical professionals.
Howard received his MD degree from the University of Western Ontario and his MS degree in Medical Information Sciences from Stanford University. He is board certified in Family Medicine. As a hobby, he enjoys following the airline industry, especially with regards to the latest schedules, routes, fares and frequent flyer programs.
Posts by Howard Strasberg
HL7 is currently balloting a draft standard for a new language to represent clinical quality and clinical decision support expressions. This new language is called Clinical Quality Language (CQL). For decades, hospital systems have used different languages to represent medical knowledge, making it difficult to author decision support applications that can be used across institutions. This problem has been previously addressed through other standards such as the Arden Syntax and GELLO, but Arden is supported only by a limited number of electronic medical record (EMR) vendors, and GELLO implementations are few and far between. The new CQL standard allows the authoring of logic for both clinical quality measurement (CQM) and clinical decision support (CDS) use cases. In addition to a human-readable form (CQL), it provides a machine-friendly representation in XML using something called the Expression Logical Model (ELM). Read further >
Consolidated Clinical Document Architecture (C-CDA) documents are being used in the United States to exchange patient data between providers. In the current issue of JAMIA, the authors D’Amore, Mandel, Kreda, et al, evaluated the quality of a sample of these documents. They conducted a detailed review of 21 C-CDA samples received from different vendors. Read further >
Last week, I had the pleasure of attending the AMIA Annual Symposium in Washington, DC. I’ve attended this meeting most years for the last 20 years, and it continues to be a great opportunity to learn about what’s new in medical informatics and to network with old and new friends and colleagues. The keynote address was given by Dr. Amy Abernethy, who discussed the importance of learning from the streams and rivers of healthcare data to make better and better decisions. She asserted that even after death, patients live on through their data, which can help other people. Read further >
Continuing on the theme of the importance of design in electronic health record (EHR) medication alerts (see my recent post here), Alissa Russ et al published a new study in JAMIA describing how an alert redesign reduced prescribing errors in a simulated environment at the VA. Read further >
Last year, I wrote about the federal (US) Health eDecisions (HeD) initiative, which resulted in various standards for clinical decision support (CDS), including an XML schema for representing a “knowledge artifact” and a Virtual Medical Record (VMR) for representing patient data. In HeD, knowledge artifacts can be event-condition-action rules, order sets, or documentation templates. Since that time, a new federal initiative has been underway called the Clinical Quality Framework (CQF). This initiative seeks to harmonize standards for CDS and clinical quality measurement (CQM). With respect to patient data models, the CDS domain has the VMR, but the quality domain has something called the Quality Data Model (QDM). One of the goals of CQF is to harmonize VMR and QDM into a single model called Quality Improvement and Clinical Knowledge (QUICK). Read further >
Three years ago on this blog, I commented on the importance of design in alerting systems, citing a study that indicated that the most important factor in alert acceptance was the quality of the display of the alert. This factor had an odds ration of 4.75, far outweighing the level of the alert (high, moderate or low risk), which had an odds ratio of 1.74. Read further >
In the United States, the Patient-Centered Outcomes Research Institute (PCORI) was created by Congress as part of the Affordable Care Act (2010) to fund comparative effectiveness research. This type of research compares different treatments to determine which treatments work best for which patients. To conduct such research on a national scale, there needs to be a way to combine the data from hospitals, clinics and patients around the country. To facilitate this type of data aggregation, PCORI recently launched a clinical research network called PCORnet. This network is described in the current issue of JAMIA by the authors Fleurence, Curtis, Califf, Platt, Selby and Brown. Read further >
A couple of years ago on this blog, I talked about the new frontier of personalized medicine based on each patient’s genomic data. We are getting closer and closer to making this vision a reality. We recently announced some exciting enhancements to our pharmacogenomics content, both in our Lexicomp Online reference database, as well as in our Medi-Span clinical decision support content and software. With these enhancements, clinicians can be alerted when ordering a drug that may be affected by a patient’s genetic variation. Read further >
Machine learning is a hot topic in today’s Big Data world. Computers in many different industries are being asked to analyze the vast amounts of data being collected by our modern technological infrastructure. One of the common problems given to these computers is to see if they can learn how to make a binary prediction from a set of input data. For example, given the details of a person’s credit history, would a bank consider them to be a good credit risk? Another example: given the words in an email message, should the email be sent automatically to the spam folder? In healthcare, an example I mentioned on this blog last year had to do with predicting whether a medication safety alert is relevant given a set of patient contextual variables. Read further >