Category Archives: data

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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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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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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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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Prediction: Pharmaceutical Litigation

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?

Historically, the pharmaceutical manufacturers have been responsible for the collection, analysis and distribution of data related to benefits and side effects of new medications. Data collection has been an extra cost of doing business. Like all costs, there is pressure to minimize these costs. If, and when, publicly available data including anecdotal evidence begins to point at a serious problem, the plaintiff’s bar also begins to develop data about risks and damage.

The cost of data collection using today’s methods imposes economic limits on the amount of data gathered by manufacturers. Cost control argues for the smallest amount of data that will likely be required, but how much is that? The smaller the data sample the less likely it is that a risk will be identified early in the life of a new medication. If a risk is limited to just some patients, a small total sample makes it unlikely that a risk to a sub-set will be identified, e.g., males over 70. If the total sample is statistically small, a sub-set will be even smaller and less reliable as an indicator of both the need for action and as a guide to appropriate action.

The push by the federal government for healthcare providers to shift from paper records to electronic healthcare records (EHRs) has been seen as a potentially slow process. The common model assumes EHRs will be implemented first in hospitals and then spread to private practices because of two factors: first, the cost and complexity of the required computer systems, and second, the lack of the standards required to move meaningful data from system-to-system for consolidation and analysis.

The maturation of the Internet in terms of processing and security now means that data can be safely moved and stored. Cloud computing—remote data processing and storage—now allows service providers to grow new services rapidly without large up-front investments. The net result is a dramatic reduction in cost for the service provider and users, e.g. physicians. As an example, one company, Practice Fusion, provides a full function EHR to private practices totally free. Step one in the changes that are occurring is unexpected reductions in cost.

Hospitals generally have short term relationships with patients whereas private practices generally have longer term relationships. Private practices monitor more patients over longer periods of time than hospitals. Step two is a shift from hospitals first to private practices first for widespread implementation of EHRs which will provide better longitudinal data about the effectiveness and risks of new medications.

The original model for EHRs envisioned small libraries of patient data sitting on the hard drives in the offices of thousands of doctors. Solutions for the nightmares associated with moving and consolidating all of that data are still on the drawing boards. Rather than “stand-alone” systems, the Internet and cloud computing allow the use of a common set of databases and a single set of standards. As an example, Practice Fusion now has seven million patient records from 70,000 medical professionals in their database. That is nearly 10% of all of the doctors in the US and growing rapidly. The data from thousands of doctors is being monitored as it is received. The data is all in a common format and is available for analysis and reporting in near-real-time. Step three is the availability of large databases with one set of standards that dramatically reduce the time and cost required to convert data to useful information.

The data that was formerly collected by the manufacturers as an additional cost of doing business is now being collected as a routine part of patients’ visits to their healthcare providers. There is little or no extra cost to track a new medication. The data is being collected by nurses and doctors trained in diagnosis and documentation as a normal part of their medical practice. Step four is further lowering of costs; step five is better quality data.

The availability of more, better, and cheaper data at lower cost offers opportunities to reduce the risks associated with new medications and may allow regulatory agencies to approve new products with less research subject to close tracking and third party analysis of results. Lower cost of research and earlier to market could represent significant cost savings for new medications.

As a new medication enters the market feedback about its effectiveness and risks will begin to flow very shortly thereafter. 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.

Manufacturers who chose not to participate in the analysis and application of this data will probably find that plaintiffs’ attorneys are using the data to find opportunities for litigation. The plaintiffs’ attorneys will probably argue that the reluctance of manufacturers to make effective use of the data justifies demands for increased punitive damages.

My prediction: more, better, faster, and cheaper data will speed up the process of moving new medications from the laboratory to patients, reduce the cost of healthcare, and reduce the damage to the bodies and lives of patients who have adverse reactions to new medications. Changes in the way that healthcare data is being collected, processed and stored will reduce both compensatory and punitive damages related to pharmaceutical litigation.

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EMRs as an Integral Part of Medical Research

The cost for collection and processing of data is a significant part of the budget for a typical medical research project. Use of data that is already being collected for other purposes provides opportunities to improve the quality of the available data, reduce the cost of obtaining it, and minimize the time required to get it to the analysts. Here’s where an EHR system can help.

As an example, a research project wants to track the use and effectiveness of a new medicine to manage a particular illness over a period of five years. Let’s call that illness Alpha. Today, research is pretty much limited to people already diagnosed with Alph unless the sample size is very large. With access to an EHR that has a large enough database, three types of patients can be tracked for the study.

The EHR can be used to find 1,000 patients who have the disease. They can be given the new medicine and tracked over the next five years using data from their EHR that is being collected as a routine part of visits to their doctor. Extra blood tests or other procedures may be required with a new medication. Reminders to the doctor can be included in the EHR and the results will then be tracked like any other data. The extra cost of obtaining and delivering the data will be relatively low.

A sub-project can be designed to get some of these patients to participate in additional research such as development of family histories of Alpha, or genetic testing. Recruiting patients for additional test through their doctors will be less costly than most of today’s means of obtaining this type of data.

The EHR can be used to find 1,000 patients who have Alpha, are demographically very similar to the first set of patients and are not part of the test of the medication. Data from their EHRs can be used to provide a baseline against which to assess changes among the patients who are taking the medicine. Again, the extra cost of obtaining and delivering the routine data will be relatively low.

The EHR can also be used to find newly diagnosed cases of Alpha over the course of the five years of the study. Newly diagnosed patients of the doctors with patients already in the study can be given the medicine and then tracked to see how effective it is if administered early. Newly diagnosed patients of doctors who are not in the study (and presumably are not aware of the medicine) can be  found and tracked to provide a dynamic baseline for early use of the medicine. There would be no extra cost to obtain the data; it is already in patient EHRs. The cost of a wider search of the database to find these cases could be noteworthy but significantly less than any other way to build a baseline of newly diagnosed patients.

There is one other piece to a complete solution and that is access to a large enough number of electronic health records to find a limited number of cases. There are a number of organizations including the VA, Kaiser, and vendors of hospital systems that have large databases. There are also physician office systems like Practice Fusion that are database driven and can quickly draw information from millions of patient records today over a much more diverse demographic (location, age, and socioeconomic status).

A research project based on EHRs will have data collected by nurses and doctors who are trained to collect health related data to assure quality. Data can be delivered to the research team in a matter of days; the interim and final research results will be available for use substantially faster than is possible with most of today’s data collection methods. When an EHR is an integral part of the research project the result is better data at lower cost faster.

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