Digital is unquestionably part of quality healthcare's very fabric. Electronic health records (EHRs) and personal fitness trackers have helped generate awareness through use. If you've followed us for any amount of time, you'll understand the healthcare trends of the future and digital is a huge part of that. This however, comes with understandable concerns of data protection. The entrepreneurial enthusiasm for the quality healthcare space is evident by the volume of medical innovation centers and angel investors. Though as we've reported there's been significant sector investment, the road to adoption of the quality healthcare innovations has been challenging and will continue to be so. Here we discuss a few areas with opportunities and what to watch out for.
According to the most recent statistics from the Office of the National Co-Ordinator, use of EHRs has increased from 20% in 2004 to 87% in 2015. EHRs were designed as documentation centers for billing and regulatory purposes. Relevant patient management data workflows has not been a priority and remains a major pain point for clinicians today. According to a study in the American Journal of Emergency Medicine, physicians spend just 28% of their time face-to-face with a patient and can go through 4000 mouse clicks in just one shift. From a provider standpoint, the regulatory and billing data entry should be performed by someone else and reduced to an invisible part of the EHR. We need EHRs which are clinically oriented with good user interfaces.
Interoperability, as defined by the Federal Office of the National Co-Ordinator for Health Information Technology, is the ability of information systems to exchange patients' electronic health information and use from other EHR systems without any special effort from the user. This then is another major point that needs to be addressed. There are hospitals with EHR systems in place which do not talk to each other or exchange information. Increasing quality healthcare consolidation of hospitals has exacerbated the problem of a lack of interopability. Health Information Exchanges (HIEs) has been woefully underfunded thus far, falling short of their own vision.
Chief Information Officers are constantly inundated with requests to purchase new technologies which will "save money, improve patient satisfaction and outcomes while decreasing readmissions." What is missing in most of these cases is evidence of these claims. The hesitation of many entrepreneurs to embrace the intuitive adoption requirement of proof of claims, is the misconception that time-consuming largely costly and randomized clinical trials are what we are referring too. This should not however, translate to "take my word for it". The traditional trials are neither practical nor necessary for most tools. Even the FDA has recently recognized that with thoughtful and cautious restraint, this is a necessary change.
"Data regarding the usage, or the potential benefits or risks, of a drug derived from sources other than randomized clinical trials. Ongoing safety surveillance, observational studies, registries, claims, and patient-centered outcomes research activities.”
Artificial Intelligence (AI)
An early definition of artificial intelligence in medicine stated "the construction of AI programs that perform diagnosis and make therapy recommendations. Unlike medical application based on other programming methods, such as purely statistical and probabilistic methods, medical AI programs are based on symbolic models of disease entities and their relationship to patient factors and clinical manifestations."
However, today a broader definition may be applied as "the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction." The use then of artificial intelligence in medicine has been subject to intense and rapid growth in interest in medical, computer science and business arenas. The market growth of AI is then based on its projected inevitability of replacement of physicians by AI technologies for a while now. BASF declared "We don't make the household product, we make the product better." This is an analogy which can be made with AI. It runs in the background of technologies already in use, yet will make these run faster and more importantly add a dimension of relevance of incoming data.
The National Cancer Institute defines personalized medicine as "a form of medicine that uses information about a person's genes, proteins and environment to prevent, diagnose, and treat disease." Medical care directed in whole or in part from information specific to an individual. Discoveries in the area of the genetics of cancer have resulted in the development of drugs no longer targeted towards an anatomical location.
A clinical trial in which drugs are given solely on the basis of genetic markers identified in cancer tissue itself is the NCI-MATCH Trial (Molecular Analysis for Therapy Choice).
“Patients with advanced solid tumors, lymphomas, or myeloma may be eligible for MATCH, once they have progressed on standard treatment for their cancer or if they have a rare cancer for which there is no standard treatment.”
The role of the personally derived data we mentioned here, will also facilitate personalized medical care. Opportunities thus exist for companies to develop more products like this for a more therapeutic approach.
By no means is this a complete discussion of opportunities for digital health. As a quality healthcare insurance brokerage we are amazed on a daily basis at the high quality clinical, financial and personal experience energies devoted to the development and advocacy for digital health tools. Long may it continue!