Tag Archives: Medical record

Expanding EMRs: Prescribed Devices and OTC Smartphone Apps

The use and scope of electronic medical records (EMRs) is being changed by evolving technology and the way it is being used by doctors, patients and people who are sometimes referred to as the Quantified Self (QS). This latter category includes people who are using a growing number of devices to record data about their physical well being, data that can be harnessed for better healthcare and healthcare research.

Traditionally, healthcare has taken one of two paths:

  • Patient problem e.g., illness-> Data: treatment-> Solution
  • Solution e.g., new medication-> Data: who needs it-> Application

For people in the QS category, there is now a third option:

  • Data-> Problem or opportunity-> Solution

The first two paths are largely driven by a doctor and their patient. They lend themselves to the capabilities of traditional EMRs. The third presents new challenges and opportunities for Personal Health Records (PHRs) linked to EMRs.

  • The first challenge is the data is originated by a patient (or potential patient who may or may not have a primary care physician.) What do they do with the data? One option is to store it until needed, but patients typically don’t have the knowledge or experience to know when it would be useful or is needed.
  • The second challenge is the potential amount of data that will be collected relative to the limited amount of data involved in traditional EMRs.
  • The third challenge is finding useful information (needles) in haystacks of data. The ability to collect data does not carry with it the ability to analyze that data.
  • The fourth challenge is managing the interface between the regulated environment in which EMRs and PHRs operate and the unregulated environment of the quantified self.

With all those challenges, why bother?

One reason is that some of the devices being developed and tested by QS’ers have applications within the EMR environment. As an example, technology being developed by Green Goose includes sensors that can be applied to pill bottles and exercise equipment to record and transmit data about usage. Is the patient really taking their medication as prescribed? Are they really getting the exercise they claim? Useful questions in healthcare delivery and research.

Another is that this data is already being widely shared on the Internet where there is only limited ability to analyze it and apply it in traditional medicine. An example is PatientsLikeMe.com which has more than 100,000 people sharing information about major chronic disease, treatments and outcomes. This is self reported data that could be made more valuable by linking it to the patient’s clinical records. Another is CureTogether.com which promises “millions of ratings comparing the real-world performance of treatments across 589 health conditions.” A third is Asthmapolis.com which offers a device that attaches to inhalers and sends time and use to a database to assist individual users and support geographic risk analysis.

An article titled The Measured Life in Technology Review notes:

The Zeo, a sleep tracking device gives its users the option of making anonymized data available for research; the result is a database orders of magnitude larger than any other repository of information on sleep stages. The vast majority of our knowledge about sleep … comes from highly controlled studies, this type of database could help to redefine healthy sleep behavior. … The data base is obviously biased, given the fact that it is limited to people who bought the Zeo … But the sample is still probably at least as diverse as the population of the typical sleep study.

Such studies obviously lack the rigor of clinical trials, but they have their own advantages. Clinical trials usually impose stringent criteria, excluding people who have conditions or take medications other than the one being studied. But self-tracking studies often include such people, so their pool of participants may better reflect actual patient populations.

This is clearly not an either/or situation. Combinations of data from patient medical records + clinical studies + self reporting offer new ways to look at healthcare and related solutions. Ways that will almost certainly contribute to improvements in quality and reduction in costs of healthcare.

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EMRs: Increasing Complexity and Capabilities IV

EMRs as networks of networks

The Internet is sometimes described as a network of networks. Electronic Health Records (EHR or EMR), like the Internet, are becoming a network of networks. Just three examples: Practice Fusion provides cloud based EHRs for physicians that facilitate the exchange of information between practitioners and patients, the later in the form of personal health records (PHRs). OmniPACS provides cloud based bio-imaging data services that facilitate sharing of information between bio-imaging service providers, doctors and patients, the later in the form of, you guessed it, PHRs. Surescripts provides e-prescribing software so a doctor can accesses the patient’s prescription benefits and medication history from all of their doctors (including patient visits to in-store clinics)   and route prescriptions to a patient’s pharmacy of choice.

A friend of mine describes it as, “the systems are transparent to the data.” What is important is the ability of the systems to capture the data, store it, protect its privacy, and make it available in various forms to meet the needs of the people who “own it” or the needs of people who are authorized by an owner to access the data. Systems will come and go and others will evolve to meet the needs of the owners and users of the data.

The growth of EMRs began with stand-alone systems and is evolving by adding network capabilities. The simplest level is the exchange of data located on a single computer between two doctors or a doctor and patient. We are rapidly moving to an environment in which the data–wherever it is located–can be shared. Specific patient data can be shared to meet the needs of the patient. De-identified general data about a patient population can be shared for research and public health.  The data can be delivered in multiple formats to meet the needs of a broad variety of users while protecting patient privacy.

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