The Obama administration has plans to release $44 billion in stimulus money aimed at getting doctors to adopt electronic health records (EHR), and the programs are helping fuel innovation in the cloud. I recently posted a story contending that cloud applications can create a statistical goldmine. This goldmine exists not only in commercial settings, but also in areas that could directly benefit all citizens, such as in electronic health records.
Not surprisingly given the size of this potential market, vendors are rushing to bring EHR solutions to market. Last month, we covered one offering from Verizon (s vz), and Google (s goog) has a similar pilot project it began in 2008. And last week, IBM (S IBM) got into the game with a product introduced jointly with Aetna (s aet).
As cloud EHR vendors have been quick to point out, digitizing patient records alone doesn’t return significant value. Simply changing the format of patient information doesn’t fundamentally change the way doctors do business. As with an on-premise EHR, a cloud-based EHR can bring value in terms of cost-cutting. But a connected cloud-based system can go much farther and bring about a reduction of medical errors and a benefit in patient outcomes. Late last month, one cloud EHR vendor, San Francisco-based Practice Fusion, released the results of a study that aggregated data across their four million patient records that provide some insights into healthcare in the U.S. In particular, the Prescription Index study focused on tracking the 20 most commonly prescribed medications by family practitioners, pediatricians and psychiatrists.
The report is an interesting insight into prescription volumes for different specialties, and looks at the highest volumes of drugs prescribed and the top-scoring medication types.
This isn’t just of interest to those involved in the pharmaceutical industry; scientists from Harvard Medical School are interested because a sample size this large offers a unique opportunity to create “nationally representative data sets” that can serve as the source data for further independent research. Some of the insights that previous cloud EHR data aggregation has drawn include:
- The link between socioeconomic status, diabetes and body mass index. To determine whether patient data from the EHR could reproduce existing knowledge about the links between socioeconomic status, BMI and the incidence of diabetes, data was extracted for all adult patients. A medical journal is going to publish the findings of this analysis.
- The patients to target in the case of a potential pandemic. Last fall, the EHR was queried to identify 300,000 patients at high risk for H1N1. That data went straight to the doctors to help them order vaccines from the Center for Disease Control. The video below details to clinicians how this process, from data collection, to analysis and through to actions for the physician, actually works in the field.
The biggest barrier to the adoption of EHR is people’s reluctance to feel secure with their most sensitive data residing somewhere unknown to them. It’s the benefits obtained from analysis (such as that presented above) that will help people to realize the value aggregated patient data can bring. This, in turn, will help cloud EHR become the norm.
Ben Kepes is an independent consultant and contributing writer for GigaOM. Please see his disclosure statement in his bio.