Category Archives: 1

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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

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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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.”

Short link: http://wp.me/pyfFd-9Q

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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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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EMRs: More, Better and Quicker Data

Electronic medical records provide value at several levels. The most basic level is a specific doctor/patient relationship. Above that is care by a doctor team and a patient dealing with a complex medical issue such as cancer. Above that is a database derived from those records from multiple doctors and teams.

Large databases already exist. The Veterans Administration has one and so does Kaiser. Just three vendors—Meditech, McKesson, and Cerner—serve more than half of the acute care hospitals that have vendor systems. Practice Fusion is the dominant service in doctors’ offices with more than five million patient records.

We are already beginning to see more and better data quicker.

A large database—more data–has a number of advantages. First, you can look at issues that affect only a very small percent of the total population and still have enough cases to draw reasonable conclusions. Second, you can define “control groups” with very similar characteristics who are not affected by whatever you are studying so you can begin to look for potential causes.

Data collected by professionally trained doctors and nurses in the normal course of their medical practice using structured formats is more reliable and easier to analyze—better data–than most of today’s studies that rely on interviews and limited records. Practice-derived data will be adequate for some studies and will provide the starting point for others. One possibility is to use practice data to find patients of interest and then work with them and their doctors to obtain additional information. The Web site PatientsLikeMe.com has already demonstrated the willingness of people to share treatment and symptom information when they see value to themselves and others.

The processes of most of today’s research require a significant period of time between data collection and publication. Large databases support near real-time analysis of data and reporting—better data quicker. Two illustrations are provided by Practice Fusion’s response to the N1H1 flu using the guidelines published by the CDC and their posting of data on Microsoft’s Azure MarketPlace.

What can happen if we get better data quicker? Here is some of what we can expect:

  • Widespread use of standardized quantifiable measures of service, effectiveness and safety; healthcare is not totally quantifiable, but much of it is and the quantifiable part can provide guidance about what works best under what circumstances.
  • Identification of significant risks and steps to reduce them such as the risks associated with center line catheter infection.
  • New ways to identify, manage, and respond to the potential risks associated with new medications and new uses for existing medications.
  • Significant impacts on medical litigation including reductions in the actual harm done to patients with subsequent reductions in compensatory damages plus better standards of care and records to reduce, and in some cases eliminate, punitive damages.

More and better data quicker from EHRs will be one of the major medical breakthroughs in the next few years.

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$250 million for the social Web including healthcare

Pioneering venture capitalist John Doerr, lionized in Silicon Valley for leading investments in Netscape, Amazon and Google, helped build the consumer Internet. Now he’s making a huge bet on the next round of the Web. … Kleiner Perkins Caufield & Byers has established a $250-million fund called the “sFund” to back social entrepreneurs who connect people online no matter where they are. …

He described the fund as a “quarter-billion-dollar party,” but its intention is serious: not to create the next Facebook but to give advice and cash to the entrepreneurs building out the social Web. Some of the areas ripe for investment are healthcare, education, mobile computing and tablets. “The third great wave of the Internet is mobile and social together,” Doerr said. “It’s going to be tectonic.”  http://goo.gl/rsDO

Patientslikeme.com may be one example. Almost certainly, these new ventures will involve the collection and distribution of medical information from more people and about more topics. Our picture of both the maintenance of health and the treatment of sickness and injury will be impacted. Our ideas about the scope and role of electronic medical records will change as will the sources and applications of the data.

Short link: http://wp.me/syfFd-530