Tag Archives: Electronic medical record

Apps & Comments

One of the virtues of the Internet is its ability to expand and respond to changing needs, interests and capabilities. A particularly good example occurs at the junction between electronic medical records (EMRs) and smart phones.

Several of my blogs have addressed the opportunities for patients to capture data and add it to their EMR and the challenges of making that information useful in the process of planning and managing the maintenance of good health and, as needed, treatment. Part of the challenge is to know what apps are available. “There’s an app for that” is now part of our language. But which one is the best one for your particular need?

There is a Wiki that will help: PHARMapps. It is structured by apps that deal with branded products and services and those that are unbranded, those for healthcare professionals and for patients, and by device operating systems. There are also classifications by topic. At this point there is no quick way to do a search by your choice of key word. Each app has an area for user comments–over time that may become the most valuable part of the Wiki.

Related:

FDA to Review Medical Smartphone Apps … the FDA is proposing a set of guidelines, outlining the types of apps that it plans to oversee. This won’t be all apps in the “Health” category, but will include those that, in the FDA’s words, “could present a risk to patients if the apps don’t work as intended.” 7/20/2011

iMeidicalApps Mobile medical apps review and comments by medical professionals

Digital Pharma: Big pharma’s iPhone apps: most recent update July 7, 2010

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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 as Part of Larger Networks

Electronic medical records provide an alternative to paper based records. They are also a source of information that can be used as part of other processes to address a wide range of healthcare issues. Here’s one example:

Congress has passed a bill requiring food processors to implement systems to track cases of food that may be related to outbreaks of food-borne illness.

An estimated 76 million people contract food-borne illnesses in the U.S. each year, with 325,000 hospitalizations and 5,000 deaths, according to the Centers for Disease Control and Prevention in Atlanta. Those illnesses cost the U.S. economy $152 billion a year in health care and related expenses. Rapid identification of the source of these illnesses and their removal from the market is critical.

Under the required tracking system, farmers would scan individual cases of produce, keeping records of where they are shipped. If a recall is ordered by the FDA, the records would be quickly disseminated to trace the current location of the recalled produce.

Once specific cases have been identified as carrying a food-borne illness, the new system will allow those cases to be removed from the market; however this is only part of a complete system. How can the illness be linked to specific cases of food? Here’s where an EMR system can help.

Most EMR systems provide for reporting of food-borne illnesses. By adding a few additional elements of information, the search for the source can be narrowed very quickly. When a doctor enters a diagnosis of food-borne illness, the system can ask for the type of food that is suspect, i.e., eggs, fish, spinach, etc., and the name of the market where the suspected food was purchased. The EMR can track doctors’ reports and when a target number of similar reports is reached an analysis can be launched. A single answer will not be helpful, but if the answers from several cases list the same food and the same market or chain, that provides a place to start. Appropriate information can be forwarded to a public agency.

Samples can be acquired, tests run, and the investigation focused on just a few likely sources. Once a case of food carrying an illness is found, the food processors’ system can be used to find all of the cases from a specific producer and they can be remove from the market.

There is one other piece to the complete solution and that is rapid access to a large enough number of records to find what may be an isolated set of incidents. There are a number of organizations including the VA, Kaiser, and vendors of hospital systems that have large databases and could report to public health agencies or the FDA. There are also physician office systems like Practice Fusion that are database driven and can quickly draw information from more than five million patient records today.

The tracking process from identification of a problem to a solution would look like this:

A Food Illness Tracking Process

This provides an illustration of the way an EMR can also be linked to other tracking systems to identify and facilitate the search for health issues such as some common types of sports injuries or automobile accident injuries. EMRs are clearly more than just systems to replace doctors’ paper records.

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