Category Archives: opportunity

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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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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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 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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The Promise of EHRs for Pharmaceutical Companies

Today pharmaceutical companies conduct extensive research to obtain Food & Drug Administration (FDA) approval of a new medication. Typically this pre-approval research is structured in three phases: Phase I, a small (20-100) group of healthy volunteers to assess safety; Phase II, larger groups (20-300) to assess how well the drug works; and, Phase III, randomized controlled trials on large patient groups (300–3,000 or more ) to assess effectiveness vis-à-vis with the current ‘gold standard’ treatment.

Phase IV trials involve the safety surveillance of a drug after it receives permission to be sold. The safety surveillance is designed to detect any rare or long-term adverse effects over a much larger patient population and longer time period than is possible during the Phase I-III clinical trials.

There are opportunities to use data from Electronic Health Records (EHR) for Phases II and III, but the larger opportunity, and the one addressed here, is Phase IV, post approval surveillance.

Theoretically it should be possible to use the growing body of data in EHRs to track patients for whom a new medicine is prescribed. However, EHR adoption is still very limited; almost all reports show adoption rates less than 10%. More important, there are more than 200 vendors providing EHRs, all of which are built using limited standards for the information to be acquired, the coding for diagnosis and treatment, and for the exchange of information.

Historically, it has proven very difficult to exchange information among multiple systems within a single organization. The generally accepted solution is an enterprise resources program (ERP) which is complex, costly, and difficult to implement.

The collection and exchange of medical information is simpler in some ways and more difficult in others. The efforts of the Federal Government to develop and implement EHR standards will help but there is a great deal to do and the final definition and implementation of standards will take time—probably several years. Even with standards, there will be minor differences among systems developed independently by a large number of vendors that will limit the industry’s ability to exchange information and will raise questions about the quality of the data.

When a significant body of common data becomes available, pharmaceutical companies should be able to use that data to track a sample of patients using a new medication in “near real time,” perhaps within a week after a doctor sees a patient. This could provide four major benefits.

• First, regulatory agencies may give earlier approval subject to effective ongoing sampling and reporting of any adverse reactions—earlier to market.
• Second, any adverse reactions could be found early and evaluated to minimize damage to patients—minimize patient harm.
• Third, early warning could lead to examination of additional data from existing medical records of patients receiving the medication relatively quickly and at relatively low cost. This will further define the risk and whether or not there are patient, disease, or treatment conditions that create or reduce risk. It may be possible to eliminate a risk just by restricting use based on definable conditions—protection for both the patient and market value of the medication.
• Fourth, timely responses to an identified risk will make it difficult for plaintiff’s attorneys to successively demand punitive damages. Compensatory damages will be smaller and punitive damages will be smaller or non-existent—reduced legal costs.

There is enough information about current and near term EHR systems for pharmaceutical companies to develop strategies to use more and better data they will offer. The time to begin the development of those strategies is now.

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Health Insurance: reform, stimulus & codes

I have been focusing on electronic medical records from the point of view of physicians and hospitals. The impact on insurance companies may be even greater. An article in McKinsey Quarterly: The new IT landscape for health insurers, August 2010 ends with the conclusion: “… CIOs will need to transform more than 90 percent of a typical payer’s IT architecture and help other executives make the corresponding changes in their business processes.”

The article provides a comprehensive analysis supported by two exhibits that define and illustrate the issues. Excerpts:

Periodically, a dramatic change in an industry enables CIOs to step up and play a decisive role in corporate affairs. We see such a seismic shift in the US health insurance industry, which faces the most sweeping changes in its half-century history. The ranks of the health care payers comprise more than 350 companies, with combined revenues of $500 billion and combined IT spending of $13 billion annually. Three principal regulatory currents are producing the impending change:

Health care reform: The legislation anticipates 30 million new individuals will join insurance rolls, while an additional 100 million will be shifting policies. The law will usher in a fundamental change to the industry’s business model. Today: 90 percent of all private policies are paid for by employers that negotiate prices and terms of coverage. The recent legislation mandates new insurance exchanges, subsidies, and tax credits that will lead millions of consumers to contract directly with the health insurance payers.

US stimulus funding: In 2009, the US Congress passed the American Recovery and Reinvestment Act (ARRA), which contains special provisions for health care IT. These reforms will first affect providers, as over the next decade health care will become rooted in readily available, comprehensive medical records and IT-based clinical decisions. …  payers’ will need to build substantial new systems that can readily interface with health information exchanges and analyze electronic health records.

ICD-10: The modern data format documenting diagnosis and procedure codes—ICD-10—was released by the UN World Health Organization in 1994. But it is overdue in the United States, where it will replace ICD-9 and expand the available number of medical codes by a factor of eight [in some cases by a factor of 144.] This change will enable a much more detailed description of diagnoses and treatments. While ICD-10 promises to improve the accuracy of medical management and claims, its adoption will force payers to undertake an effort likely to exceed that of the Y2K campaign. Yet while the costs of adopting ICD-10 are significant, the potential regulatory penalties for failing to adopt will make it a necessity.

There are concerns being expressed in the medical community about the availability of resources to provide the infrastructure required for electronic medical records. Add the demands for resources required by insurance companies and the outlook is even more grim, unless of course, you are one of those needed resources.

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