.b

How do you introduce a class like Mindfulness to school children? You start where they are and make it meaningful to them. This is a link to such a story, a story that also provides a simple do-it-yourself app for your cell phone. An app I now use and you may want to too.

Why an app? Because texting is the “in” form of communication at the personal level for school kids. The app is a simple text message: .b

In England where the story started, a period at the end of sentence is often referred to as a full stop. The common focus of Mindfulness, particularly for the beginner is “breath.” Text .b and it is received as a reminder to: STOP! Breath, continue.

Simple message – simply delivered – simply profound.

A TED Talk: Mindfulness in Schools: Richard Burnett at TEDxWhitechapel
Published Feb 14, 2013 http://www.youtube.com/watch?v=6mlk6xD_xAQ

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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EHR Tracking for Drugs and Devices

Cloud-based, integrated electronic health records (EHR or EMR) systems provide a new paradigm for the collection and use of data for long term research about drugs and devices after FDA approval—commonly referred to as Phase IV testing.

The new paradigm is particularly relevant for drugs and devices for chronic diseases and conditions because long term use may lead to adverse events that do not occur during the shorter term testing required prior to FDA approval. The significance of chronic conditions is indicated by the following statistics:

• Chronic diseases cause 7 in 10 deaths each year in the United States.
• About 133 million Americans, nearly 1 in 2 adults, live with at least one chronic illness.
• More than 75% of health care costs are due to chronic conditions.

The primary differences between the EHR based paradigm and traditional Phase IV testing are listed here (Notes and comments are in italics.)

EHR description: Data is drawn from a database that provides for multiple uses of the data to maximize value.
Traditional Process: Data is collected for a single use—little or no cost sharing.

EHR description: A wide range of available data is identified and collected to provide flexibility during analysis.
Traditional Process: A narrow and focused range of data is collected to manage cost.

EHR description: Samples of existing data are used to validate test objectives and methods early in the research project.
Traditional Process: Prior similar studies and assumptions are used to validate objectives and methods.
Breakthrough drugs or devices may require significantly different objectives and/or methods than required in prior tests.

EHR description: Test subjects—people with the disease—and matching control subjects can be found in the database.
Traditional Process: Test subjects and matching control subjects must be found using a variety of means.
A computer can find test subjects in an existing database very quickly and at very low cost. Traditional processes for finding and recruiting test subjects are significantly more costly and time consuming.

EHR description: Data collection and analysis is conducted in accordance with HIPAA privacy requirements and patient participation is not required.
Traditional Process: Patients must be contacted and enrolled in the data collection process.
The initial data is available in the database day-one of the test to minimize start time and cost.

EHR description: Data collection is totally independent of and not biased by the research.
Traditional Process: Data collection may be suspect under some circumstances.
The process of contacting and enrolling patients may lead to changes in behavior such as greater care in taking drugs as prescribed and greater participation in follow-up visits. Independent data collection may provide a sample more closely aligned with actual patient behavior.

EHR description: Able to add new test and control subjects as needed including their history from the database.
Traditional Process: Ability to add new test and control subjects with past data is very limited.
If adverse events are concentrated in patients with a defined set of characteristics it may be necessary to expand the number of patients with those characteristics in both the test and control group to assure statistically reliable results and a sufficiently large sample to meet the HIPPA privacy rule.

EHR description: Additional historic data can be added from the existing database if preliminary analysis indicates it may be significant, e.g., prior use of similar drugs or a history of another ailment.
Traditional Process: Very difficult to obtain additional historic data from patient records late in study.
If adverse events are found in patients with defined prior conditions including prior use of similar drugs, exposure to other diseases, etc., the database can be searched to add subjects and findings related to that topic for further study and to assure statistical reliability.

EHR description: Off-label uses can be tracked through analysis of electronic medical records and prescriptions.
Traditional Process: Off-label uses are difficult to track based on paper records.
Off-label use can identify new marketing opportunities and a second parallel test can be conducted at relatively low cost to assure continued safety and effectiveness of both uses.

EHR description: If the drug or device has been on the market for some time historic and future data can be concatenated to expand the test horizon.
Traditional Process: It is difficult to find and map historic data to a current test.
Is a newly discovered side affect new or just newly discovered? Does a longer period of documented use show greater risk?

EHR description: The entire database can be used to assure the test and control groups are representative of all patients who are taking the drug or using the device.
Traditional Process: The study itself does not provide means to assure a representative sample over time.
The patient population may change over time; the test should change to assure that the entire patient population is adequately represented.

EHR description: Analysis can be conducted in near-real-time to minimize damage to patients that may occur between the time an adverse reaction is discovered and the time appropriate action is taken.
Traditional Process: Traditional analysis moves at the speed of paper which can delay appropriate actions to minimize damages.
Quicker response to adverse reactions reduces cost and provides an opportunity to demonstrate concern for patient well being to minimize punitive damages.

Cloud based, integrated electronic health records offer a new paradigm for tracking drugs and devices after FDA approval. This approach allows more testing to be done at lower cost. It supports faster analysis to minimize actual damages to patients and subsequent compensatory and punitive damages awarded to plaintiffs’ and their attorneys. Better healthcare at lower cost.

Originally published under this title on EHRbloggers, October 12, 2011

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Expanding the Scope of EHRs II

Morgenthaler Ventures, a premier venture capital firm, announced August 25, 2011, the 11 finalists of their nationwide contest to find the most promising health IT startups looking for seed and Series-A funding. The contest was organized by Morgenthaler Ventures with co-organizers Silicon Valley Bank, Health 2.0 and Practice Fusion.

There are 22 finalists, 21 of which probably (or potentially) could link to a patient’s EHR. My speculation for each product is indicated in italics. Speculation, not knowledge about specific products, based in part on my two recent posts: Expanding the Scope of EMRs and Devices, Applications and EHRs

These products provide more evidence that EHRs must be designed to interface with “data devices” and apps that go way beyond the scope of most traditional EHRs systems.

Seed-Stage Finalists:

Careticker is the world’s first platform that helps patients plan in advance for a hospital or outpatient procedure. (Miami, FL) probable link to EHR for information

EyeNetra is the most affordable mobile eye diagnostic ever developed, allowing anyone to take their own eye test, get a prescription for glasses, and connect to eye-care providers all on a mobile phone. (Cambridge, MA) add results to EHR for complete record

Skimble powers the mobile wellness movement with a cross-platform ecosystem of fun and dynamic coaching applications. Its latest title, Workout Trainer, ranks Top 10 in the free Healthcare & Fitness category on iPhone/iPad.  (San Francisco, CA) possible data capture for EHR

SurgiChart is a mobile, cloud-based, social-clinical network for surgeons to exchange relevant perioperative, case-centric information. (Nashville, TN) could include data from or links to EHR

Telethrive provides patients an instant connection to doctors for a medical consultation using any telephone or computer with complete audio and video conferencing.  (Los Angeles, CA) possible links to EHR for data and recording results

Viewics provides hospitals with cloud-based analytics and business intelligence solutions which enable them to drive enhanced operational, financial and clinical outcomes.  (San Francisco, CA) probable links to EHR for data

Series-A Finalists:

 AbilTo develops and delivers online mental health programs to managed care members and enterprise workforces that help reduce payor costs while improving overall health outcomes. (New York, NY) probable links to EHR for de-identified data

Axial Exchange moves healthcare organizations towards pay-for-performance, enabling providers to coordinate care and measure clinical quality across disparate settings. (Raleigh, NC) EHR as source of data about treatment and results

Empower Interactive‘s online services deliver proven psychotherapy methodologies via an e-learning platform to greatly improve the economics and accessibility of mental and behavioral health solutions. (San Francisco, CA) potential links to EHR for treatment planning and results

Jiff is the first HIPAA-compliant iPad platform for patient education in the medical industry –used by doctors, nurses, patients and more. (San Francisco, CA) could be driven by data in patient’s EHR

YourNurseIsOn.com employs bi-directional text, phone and email communications to help hospitals and agencies put “the right healthcare providers, in the right places, right now.” (New Haven, CT) no apparent link to an EHR, interesting that this was at the end of the list.

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EMRs Impact on Pharmaceutical Litigation

Electronic Health Records or Electronic Medical Records (EHRs/EMRs) are providing information that is impacting risks, risk management and litigation related to pharmaceuticals. I have posted several articles about the legal impact of EHRs at EHRbloggers.com This page provides links with comments about those posts.

My interest in these issues is based on my commitment to reducing costs and improving quality of healthcare. It is supported by my experience with litigation support consulting assignments. I am not an attorney and this material is not offered as legal advice. For legal advice, consult an attorney.

EHR Technology Driving Drug Safety

March 4, 201
Pharmaceutical litigation is where huge lawsuits are common. I predict that this will change and the change will reduce the damage to the bodies and lives of patients, reduce the cost of healthcare, and speed up the process of moving new medications from the laboratory to patients. What will cause that change? …

As a new medication enters the market feedback about its effectiveness and risks will begin to flow very shortly thereafter [in EHRs.] Manufacturers who chose will be able to measure and document effectiveness and will also be able to identify risks. As the patient population using the medication grows, the sample being tracked will also grow. This means that that as risks become significantly large—however significance is determined—the data base will be growing to support both risk assessment and mitigation. Manufacturers, if they chose, will have new opportunities to minimize damage by providing additional information to the medical community and patients and to develop specific responses to specific problems. Timely action will reduce compensatory damages and responsible action will reduce punitive damages. Timely responsible action will reduce damage to the brand in the marketplace.

EHRs Meet Pharma’s Need for Better Risk Management

May 26, 2011
Pharmaceutical product liability lawsuits are notoriously large and represent a major threat to the commercial success of new medications for years after they are introduced. There are new tools available to manage and reduce those risks. … Actual damages provide the basis for lawsuits but punitive damages are often a large part of the final settlement. Early indicators of potential damages offer opportunities to demonstrate concern for patient safety and thereby reduce or avoid punitive damages. … “We didn’t know about the risk,” is no longer a defense, if it ever was one.

EHRs Meet Pharma’s Need for Post-Approval Research

May 23, 2011
Historically, the identification of members of sub-groups and acquisition of additional information has been prohibitively expensive. Rapidly evolving, fully networked, cloud based EMR systems such as Practice Fusion are now being used to collect this data as part of the physician’s normal practice. Data collection, the most costly part of the research process, is now essentially free.

Using data captured by an EHR vendor avoids any real or implied relationship between the doctor who captures it and the pharmaceutical company that uses it. This lends credibility to the outcome by eliminating any appearance of undue influence by the pharmaceutical company on the conduct or outcome of the research.

Personal Health Records Make Care Safer and Cheaper,

April 21, 2011
OTC manufacturers and distributors should find de-identified information about their products and any side-effects or unexpected benefits valuable. … Even a symptom with low frequency can be significant if the harm done to a few individuals is substantial, as it was in that case.

Electronic Health Records and Securities Fraud

April 12, 2011
Can a failure to disclose knowledge about a medication’s side-effects lead to a successful suit for securities fraud? In March 2011 the Supreme Court ruled that it can. … The availability of information from electronic health records with large, near-real-time databases like Practice Fusion’s expands the “total mix of information… available.”

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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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EHRs Support FDA Accelerated Approval

There are provisions for accelerated approval by the Federal Drug Administration (FDA) of new drugs for patients with serious illnesses. As an example, Genentech’s Avastin for metastatic breast cancer received accelerated approval on February 22, 2008. On June 29, 2011, the FDA’s Oncologic Drugs Advisory Committee recommended 6 to 0 that the approval be withdrawn. As of this posting, the FDA has not announced a decision.

The approval was based on standard research for this type of medicine and showed sufficient benefit that accelerated approval was granted. Subsequent similar studies raised questions about effectiveness and risks that led to the recommendation for withdrawal.

Standard research methods have been tested and generally provide the required information for FDA action. However, samples are small—the largest sample in this series of tests was fewer than 800. Most of the critical measures are in the low single digit percentages which mean that errors induced by small sample size can be significant.

Recent advances in electronic healthcare records (EHRs) are creating a new parallel path to provide much larger samples to assist in the assessment of traditional research. What ERHs lack in precision, they make up in breadth in both the research target group and control groups. This greater breadth allows for analysis of factors that may contribute to adverse events or produce higher rates of effectiveness among sub-groups in the target population.

There are, according to material presented at the hearings, 45,000 patients diagnosed with HER2-negative matasticised breast cancer every year. All of these patients have healthcares records although most of them are probably not yet in electronic format. Reasonable projections based on recent history and the continued efforts by the US government to encourage the use of electronic records suggest that EHR sample sizes for a population of this size could be well over 1,000 records in the immediate future and in the foreseeable future significantly greater.

Research based on EHRs is relatively inexpensive compared to traditional research because the cost of data collection has already been covered. There are additional advantages as outlined in EHRs Meet Pharma’s Need for Longitudinal Research. I am not suggesting replacing traditional research with EHR based research, but rather, doing both in parallel. A model of such a testing program might look like this:

During Drug Development: Use EHR based research to assist in defining the scope and nature of the potential market for a new drug as soon as there is high probability that a request for FDA accelerated approval will be made. Begin to build a control group that includes all potential variables including current and prior medications used for the target disease. Maximize the size of the control group to minimize sampling error there. Include relevant findings from this analysis as part of the request for accelerated approval and agree to continue the research after approval with the addition of patients for whom the product is prescribed.

At Launch: Begin tracking weekly for market research to gain insights about who is prescribing it and for which patients. Also track any adverse events to manage risk.

Three To Six Months Out: Reduce the frequency of reports to monthly or quarterly depending on market research needs and medical research requirements. Look for clusters of adverse results and clusters of effectiveness. Expand the control group with sub-groups to maintain sampling validity as smaller clusters are explored in depth. Explore options to reduce adverse effects including notices. Explore opportunities to focus marketing aimed at other clusters to improve overall performance in terms of satisfying FDA requirements and improving market penetration.

Map the EHR results to those from traditional research to build credibility for the smaller sample sizes of the traditional research or to explain any differences and their significance for FDA review and approval.

After FDA Approval: Continue the EHR research to manage risks associated with adverse events, continue to develop the market, and find new opportunities for new or improved products.

The rapid evolution and implementation of Web based, fully networked, database driven EHRs like Practice Fusion are creating a new set of tools to facilitate accelerated release and post-release research leading to final approval.

 

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Cloud Based EMRs: Better Post-FDA-Approval Research

A recently closed longitudinal study of a medication to boost “good” HDL cholesterol concluded: “… that the HDL-boosting drug niacin failed to cut the risk for heart attacks and strokes.” The study was designed to track patients for 4 to 6 years but was terminated 18 months early based on the results to that point. The cost: $52.7 million or $15,500 per person for the 3,400 study participants. Fully networked, cloud based, electronic medical records (EMR) appear to offer a better solution.

The topic of this post is research about medications that have received FDA approval and are being prescribed for general use. The subject of a research study could be a new medication being tracked for purposes of risk management among patients who were not fully represented in the limited sample used to obtain FDA approval. It could also be an established medication where adverse events are suggesting that more needs to be learned or where there is reason to believe it is not significantly effective to justify continued sale. In this case, it was a matter of both effectiveness and risk.

The key to a better solution is the evolution of fully networked, cloud based EHRs that create a database that is large enough to provide meaningful statistics about specific occurrences. As an example, Practice Fusion now hosts electronic records created by 90,000 medical providers in a single database that has more than 12 million patient records and is growing. The data is being collected as part of physicians’ normal practice—the electronic version of historically hand written notes.

There are operational advantages:

• Data collection is conducted to serve the day-to-day needs of the physician and their staff so they have established procedures and a vested interest in quality.
• The use of the data is totally independent of its collection so there is no bias in the data collection process or the data; neither the doctor nor the patients are even aware of how the data may be used: a totally blind process.
• Separation of data collection and use remove any presumption of undue influence by the sponsor of the study.
• Data is uploaded by physicians daily so it can be made available in near real-time for use at checkpoints in the study.
• If an area of particular interest is discovered, e.g., women over 60 who are more than 20 pounds overweight, additional participants with those characteristics can be identified and added to provide a larger, more reliable sample of that group.
• The study can provide information about risks and effectiveness, increased levels of HDL, and continued use, i.e., prescription renewal.
• In many cases, patient history related to their disease including prior medications is available and information will be available about patients who stop taking the medication or change to a different medication.
• The same database can be used to create a control group of patients that are not taking the medication and have essentially the same medical conditions and demographics as those who are; if a member of the control group begins taking the medication they can be moved to the study group and replaced in the control group—there is no need to deny patients the opportunity to take the medication just to protect the integrity of the data.

The largest benefit is that there is no marginal cost for data collection. The data is already being collected. There are, of course, charges for extraction of the specific data required for the study, for HIPAA compliant de-identification of the data, and for the use of the data. The study costs cited in the introduction, $15,500 per participant, include costs in addition to data collection, but data collection is a major part of that cost and could be dramatically reduced through the use of fully networked, cloud based, electronic health records.

Better data for the reasons noted above at lower cost translate to better healthcare at lower cost.

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Related articles

• FDA approves cancer drugs quickly, report says (Morning Read) (medcitynews.com)

Medical Records Are Just One Form of Healthcare Information.

In just a few years medicine has taken major steps to move from paper records in individual doctor’s offices to electronic medical records that can be shared with patients and their other physicians. The primary point of coordination of care among a patient’s physicians is moving from the patient—I saw Dr. Adams last month and he told me…—to the patient and her team of physicians who share the same records. The good news is each physician now has more information; the bad news is they have no additional time to analyze it. That is the tip of the iceberg.

At the same time, the marketplace has seen the value of health related information and is rapidly creating new ways to collect that information. Sources as diverse at private individuals working on smart phone apps to corporate giants like Ford Motor, Intel and General Electric are creating new ways to capture more and more health related information. Most of this will be routine but some of the haystacks will have needles of data critical to a healthy future for some patients or even life savings requirements.

Physicians can choose what medical information to collect depending on each patient’s specific circumstances; they can decide what is relevant and influence the volume of physician generated medical information. Patients will make the decisions about the information to be collected from many of the marketplace solutions: Here are my vital signs taken during my bicycle ride last week when the temperature was 89 to 95 degrees and I climbed 1321 feet and averaged 13.2 miles per hour. Here is the data from my Ford car last month and the statistics Intel/GE captured with regard to when I took my medication and how I answered the phone. How good is the data? Are there any needles of critical data?

We are rapidly moving from inadequate amounts of data to overwhelming amounts.

Prior solutions are the source of almost all significant problems. There is a problem on the horizon and now is the time to explore ways to manage the growing amount of data being generated and shared within the medical community and the marketplace. The people who are harnessing computers to capture medical data may be the logical ones to develop ways to capture, assess, integrate and analyze the growing amounts of data or the solution may be out in the marketplace. Stay tuned.

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Expanding EHRs to Include OTC Meds

My recent post about the Supreme Court’s decision in the case involving over-the-counter Zicam cold remedy got me thinking about over-the-counter (OTC) medication and personal health records (PHRs). PHRs are a logical next step in the evolving application of computer systems to capture, analyze and use healthcare information. In fact, many EHRs, including Practice Fusion, provide patients with PHRs. But, neither the EHRs nor the PHRs I have seen incorporate information about OTC medication and devices yet. How could that be done?

Some EHRs, including Practice Fusion provide two pieces to the puzzle: electronic transmission of prescriptions to my local pharmacy and an electronic file for my data: a PHR. My pharmacy provides two pieces: a frequent shopper card (FSC) and they upload prescription data to my PHR. The basic links and basic data provide a good starting point.

A workable system might look a lot like this: My doctor’s EHR company provides me with a personal health record. The EHR company and my pharmacy enter into an agreement to exchange data. I designate my PHR as the destination for my pharmacy’s transmissions.

Near the end of my next visit to my doctor sends two electronic prescriptions to my pharmacy; I pick them up. No change there, I can do that today. My pharmacist notes that one medication is for high blood pressure and recommends a blood pressure monitor that will transmit the results to my PHR. I pay for the prescriptions, monitor and a magazine using my FSC and credit card. The pharmacy uses the information associated with my FSC to send a confirmation of the purchase of my prescription including the last four numbers of each prescription (just in case I only picked up one of the two prescriptions). The pharmacy uses the same information from my FSC card and their inventory system to report that I purchased a blood pressure monitor that can transmit the readings. It does not mention the magazine. My personal health record forwards the information to my doctor’s EHR.

A month later, I refill my prescriptions and pick up an OTC medication. The pharmacy uses information from my FSC to send a confirmation to my doctor that I picked up my prescriptions. My doctor now knows I am continuing to take the medication. Her electronic health record can track my continued purchases or my failure to continue and recommend appropriate action. My pharmacy uses their FSC tracking system and inventory system to capture the information about the OTC medication and reports that to my PHR which forwards all of the information to my doctor’s EHR.

My doctor’s EHR checks the OTC meds for potential harmful interactions with my prescriptions and any other OTC meds I have reported. It also checks for any alerts such as evidence that Zicam caused people to lose their sense of smell. The EHR alerts my doctor and/or me if appropriate.
On my next visit my doctor sees that I now have a blood pressure monitor that can transmit data and suggests that I check my pressure weekly and send the information to my PHR. My doctors EHR will pick up the data and track any significant changes.
On the visit after that, my doctor is considering a change in my blood pressure medication but notes that there is a potentially harmful interaction with the OTC medication I bought. She now has information that will allow her to recommend I stop taking the OTC meds, she can select an alternative medication, she can ask me to check my blood pressure and send the data daily, or a number of other options. More and better health data gives my doctor and me more options.
So how do I keep my meds separate from my wife’s? My doctor’s EHR sets up a “family PHR.” My prescriptions with my name on them are reported to my PHR and everything else is reported to my family’s PHR. My wife or I or both of us move items from the family PHR to our own with a click of a mouse. My doctors EHR can send an alert to me if it finds a potentially harmful interaction with my medications and some on my family PHR. I can then unlink that med from my name (it was for my wife or one of the kids) or link it to my name so appropriate actions can be recommended to my doctor and/or me.
If I go to another doctor about an unrelated matter and he uses the same EHR I can give him direct access to my PHR. Alternatively, I can have records sent from my PHR to my new specialist and request appropriate records from him so I can update my PHR. If my new specialist uses the same EHR, I now have a medical team that always has current information about my conditions, prescriptions and OTC meds and available devices.
My doctors and I now have better information to provide healthcare at lower cost.
My pharmacy is now contributing information that gives me and my family a strong reason to give them all of our non-medical healthcare related business. Almost any FSC based system will have some capability to analyze purchases vis-à-vis individual card holders. The tabulation and transmission of the required data should be evolutionary, not revolutionary.
OTC manufacturers and distributors should find de-identified information about their products and any side-effects or unexpected benefits valuable. In the Zicam case the record showed that there was no loss of the sense of smell among the members of the company’s research group. Even a symptom with low frequency can be significant if the harm done to a few individuals is substantial, as it was in that case.
EHRs are still in the process of using the full capabilities of the Internet to gather, analyze and distribute data that will improve healthcare and reduce costs. This is just one of many near-term future opportunities.

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