Real-Time Market Size

Real-Time Market Size.

In this series, the potential implications of technology that works at the point of care in real-time are discussed. In a prior blog, the power of the physician at the POC is profound, directing the flow of 2 trillion dollars in discretionary treatment protocols. The disparity of treatment for coronary artery disease in different portions of the nation was discussed, based on data in the Dartmouth Survey of healthcare. In this edition, the power of the physician at the POC is discussed in the context of the emerging new area of personalized medicine.

Personalized medicine is when the specific lab, imaging and genetic testing indicate a better fit for one medicine than another. The Pharmaceutical industry is rapidly developing new medications to improve outcome of patients in oncology diseases, hepatitis, diabetes, rheumatology and other conditions. These personalized treatments produce better outcomes but carry an enormous cost to the patient and third-party payors. For example, new treatments for hepatitis can be 20-50x more expensive than standard treatment but is twice as effective. Darrell West, a member of the President’s Council of Advisors on Science and Technology adds: “Implementation of these regimens creates a situation where treatments are better targeted, health systems save money by identifying therapies not likely to be effective for particular people, and researchers have a better understanding of comparative effectiveness.”

Cost is not the only obstacle to the era of personalized medicine. A second major hurdle to better information management of the medical record is the fact that much of the data resides in the unstructured portion of the medical record. Recent implementation of EHR has made digital compilation and maintenance of disease registries more feasible. EHRs still provide only a small portion of the medical data in structured format, between 20-30%, with the remainder inaccessible to electronic analysis. Unstructured data in the medical record also takes the form of nursing and physician progress notes, consult notes, admission notes, discharge summaries, pathology reports and operative reports.

The implications of this emerging era of personalized medicine will have profound impacts on other segments of the healthcare industry like pharmaceutical and IT companies. According to Pfizer Chairman and CEO Jeff Kindler, “The changes we’re making recognize the end of the era of ‘blockbusters’—medicines that meet widespread primary health care needs and therefore generate billions in revenues…. We are focusing on addressing many specialized needs of many smaller groups of people. This personalized medicine approach means new opportunities for people who are sick, for physicians and for our shareholders.” Personalized medicine may change the investment calculus in pharmaceutical companies that rely on blockbuster marketing while enhancing investment opportunities in smaller, newer companies that develop more targeted medicines that can justify a higher profit/cost basis.

Personalized medicine will also tremendously affect the medical informatics industry. The need to correlate latest developments in oncology research literature with a dynamic patient condition has stimulated the application of IBM artificial intelligence (Dr Watson) for future clinical cancer management at Sloan Kettering and MD Anderson, according to the Washington Post, June 27, 2015. Dr Watson is designed to work outside of the EHR, after clinical data has been extracted and sent to Dr Watson for backend analysis. It is not designed to work at the point of care in real-time.

Technology however exists today that can accomplish much of what Dr Watson does but at the point of care in real-time within the EHR. Hiteks NLP technology is capable of developing this comprehensive patient-specific picture suitable for personalized medication recommendations. In its document improvement application, it has shown the capability to use algorithms at the point of care in real-time to influence clinical decision support. Treatment algorithms to support personalized medications at the point of care can be utilized by Hiteks application in a plug and play format, making it easy for the end-user to employ the latest clinical trial outcomes. This is in contrast to Dr Watson, which is not modular, requiring extensive rewriting of code to accommodate latest knowledge.

In a white paper in January, 2014, IBM CEO Virginia Rometty said she wants Dr Watson to generate $10 billion in annual revenue within ten years. This revenue is based on the limited market that Dr Watson is designed to address, back end analysis. Hiteks Real-time technology can accomplish everything that Dr Watson can but in real-time at the point of care and at a significantly lower price point. The financial estimates for this market might even exceed the Dr Watson targeted market.

Real-time intervention

Real-time intervention in health care has been attempted before with poor results. What makes us think that we can do better this time?

Well, to start with, we have learned from previous experience that inundating clinicians with false-positive information and information that they already know but does not change their clinical practice is a no-no (see drug-drug interactions).

Clarifications of their documentation to support better billing and quality reporting, however, are seen as non-intrusive to their clinical care but improves the administrative practice.

Although clinical decision support is a requirement under Stage 2 Meaningful Use.
According to HealthIT.gov, and includes tools such as “computerized alerts and reminders to care providers and patients; clinical guidelines; condition-specific order sets; focused patient data reports and summaries; documentation templates; diagnostic support, and contextually relevant reference information,” these systems are not likely to be used unless the False Positive Rate is significantly reduced based on real clinical experience.

Only through machine learning, prescriptive and predictive analytics can the sensitivity of the systems be improved. Translational research has been used to apply new learnings from retrospective database research. The CDS Consortium, at the Vanderbilt has identified “knowledge translation and specification” as an important research objectives. In addition, the Consortium for Healthcare Informatics Research, a VA-based research program located at the University of Utah is also advancing many of the methods required to conduct better predictive and prescriptive analytics.

In the meantime, real-time alerts can help providers avoid mistakes and improve quality by focusing on a few set of prioritized reminders and notifications related to Performance Indicators which have been studied extensively by AHRQ, JCAHO and CMS, amongst others. Core Measures and Patient Safety Indicators are 2 general domains which are important to notify providers of relevant information.

Blueprint Accelerator Boosts Hiteks

Blueprint Accelerator Boosts Hiteks

Hiteks entered the Blueprint Accelerator Program in July 2014, from which it received a small financial investment but a large emotional investment. What I mean is that the Blueprint Fund and its Management, a group of young Ivy-league health care entrepreneurs, shook up Hiteks’ approach to sales and revenue-generation. Their goal was to show the founding members of a start-up organization how to focus on a repeatable sales methodology which would validate (or not) the current business model.

Up until Blueprint, Hiteks had been engaged by health care organizations interested in better accessing either their own data or real-world data that Hiteks had the right to de-identify and share. Clinical NLP technology was in its early phases, most people thinking that its true value was in retrospective analysis of Big Data to produce population-level insight into quality, better-disease understanding, or to analyze risk. Although these have certainly proven to be important areas of application of the technology, they all have a limited (and competitive) nature to them since they don’t impact clinical care or workflow.

What we found by talking endlessly to all of the Blueprint alumni and network is that there was a muted interest level when it came to retrospective analysis of data. Not that it wouldn’t be helpful, but a software company needs to produce code to conduct repeatable processes, not one-off questions and poorly defined hypothesis-driven search for answers. Most of the NLP companies that Hiteks compared its technology to at the time fell into the trap of designing their systems to facilitate such retrospective analytics. The Blueprint management urged against any technology marketing and sales that couldn’t be repeated 1000 times by the same application and where demand was greatest.

That’s when Hiteks realized it had to apply its technological know-how to the real-time decision-making of the clinician at the point of care. This was the only step in the health care delivery process which allowed for the validation of data points to become part of the medical record. It would have to be fast enough for clinicians to quickly make a decision on the content of the NLP output so that their workflow would not be hindered. It would have to be integrated into their existing documentation system, the EHR, so that they wouldn’t have to use yet another system to give them advice.

Once the real focus of Hiteks was realized and agreed upon by its founders, communicating the value of the technology and the company was the other area that was developed and shaped with the help of Blueprint. Once we got the email lists of prospective leads organized from multiple sources, we sent out blast emails which got the 3% traction/response that we were told to expect. This meant that we found the early adopters of our solutions, and we could focus on selling to them the value proposition we created. This helped us get our first 3 health system clients for a total of 50 hospitals and affiliated clinics signed up to use our software. The rest is history…

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