Considering the embryonic stage of Healthcare IT overall, it’s no surprise that there are multiple loosely-defined phrases are being used to describe emerging trends, products and services. This page is an ongoing list of some of these rudimentary themes and buzzwords. Sometimes they are valid concepts that point to a viable new pattern. Sometimes they are marketing messages propagated through important or technical sounding terms, mostly to impress laypersons. Irrespective of the underlying intention, each potentially serves as a data point in the collective experimentation, definition and evolution happening in the innovation microcosm of Healthcare Information Technology.
This list is maintained primarily with an intention to catalog, not to endorse or validate. Take each item with a grain of salt.
Self-aggregation, Self-reporting, Social discovery
Individuals affected by a common condition come together to form a self-governed community that shares data about that condition. Participants do self-reporting of all relevant data, which can range from quantitative physiological parameters to more qualitative attributes like emotions. The ultimate idea is to facilitate social discovery of pertinent facts about preventing, coping and curing the condition in question. This trend may not yet have the explicit endorsement or validation from the scientific medical community (that tends to think in terms of rigorous clinical trials), but it’ll likely be a key force in the medical knowledge evolves in future. The ‘evidence’ in Evidence Based Medicine should not be restricted to just what the healthcare delivery system can provide. Examples: Curetogether, PatientsLikeMe, Ben’s Friends.
There are times when knowing how you fare against other people like you (or in the same condition as you) can be a valuable insight- not for a clinical or therapeutic reason, but from a lifestyle management perspective. The services and products in this category are trying to create communities of users with some condition in common. Once together in a large group, de-identified collective data can be analyzed to give individual user an idea of where he/she stands. Of course, there are added benefits: being able to share best practices (what works/doesnt work), stories, find connections etc. But the primary underlying theme here is to measure and rank something related to health or wellness that is not obviously quantifiable otherwise. Examples: Zeo (esp. the ZQ score they offer), FitBit, DirectLife.
There are repetitive tasks and regimens that individuals need to adhere to, as a part of treatment and/or prevention. Most obvious use case in this category would be around taking medications – right dose, right time, right way. A number of offerings are surfacing with the value proposition of helping an individual stick to a given health-related task schedule. Example: Glowcaps, PictureRx, RememberItNow!, PillPhone for medication adherence support. Qwitter may be a bit peripheral example, given its specifically for smoking cessation support.
One of the fundamental issues in health is how to affect behavior. Each of us makes countless little (micro)choices every day as we go about living our lifestyle. For example taking stairs instead of elevator, drinking water before we feel thirsty, taking breaks from the hunched-over desk job position every couple of hours. One the most popular themes today is designing services that help monitor and influence these microchoices, steering them towards a healthier selection. Commercial examples: MeYouHealth, ContagionHealth. Research examples: Health Games Research, HealthGamers, UbiFit Project.
Knowledge Discovery From Online Sources
This one is a bit harder to explain. The basic idea is to continuously monitor online health-related information sources (blogs, news sites, government sites, etc.) and analyze that free flow of data to figure out useful facts, trends and patterns overall. The analysis usually involves advanced techniques like bayesian logic, natural language processing, outlier generation and pattern recognition. In November 2009, the Decision Support Systems journal published a very relevant paper on ‘Automatic online news monitoring and classification for syndromic surveillance’ in its Volume 47, issue 4 (link here) that gives an academic perspective of this theme. Example: Google FluTrends and Healthmap do it for public health disease surveillance, whereas the Israeli startup First life Research plans to do it for medication related patient-generated online content. An interesting research paper from Southeastern Louisiana University on forecasting future flu rates using twitter message analysis. Docphin takes a different approach – no advanced analysis, but smart organization and presentation to make knowledge consumption more personal.
Self-selection, Self-diagnosis, Self-referral
Here the individual users use the given service to match and align themselves to something. Yes, it could have various forms of overlap with self-aggregation or self-reporting, but the main intent here is to enable individuals to sort/match themselves. The service often survives by taking a cut from this match-making transaction. Examples: iTriage, TrialX. A specific instance of this theme can be around disease diagnosis- where individuals use a particular service to diagnose themselves. Examples: FreeMD, MEDgle.
Enhanced Data Entry
Local clinical workflows are being taken over by EHRs, and clinicians spend a lot of time begrudgingly entering data into tedious forms and templates. A number of startups are trying to make this easier, by creating a usable, more efficient layer between the end-user and the incumbent EHR user interface. At the heart of these new offerings are technologies like NLP and Speech Recognition. Examples: Dragon Medical by Nuance, Health Fidelity (based on Columbia’s MedLEE engine), Medicomp, MModal, CliniThink (you can find the complete ongoing list on Multiplyd Wiki). An interesting offshoot is Tonic Health which enhances non-clinical data entry for patients, providing an interesting ‘consumer engagement’ value proposition.
Thanks to HITECH and Healthcare Reform, EHRs are conventional Healthcare IT tools and mainstream EHR founders are billionaires. Perhaps its logical that the pendulum to swing from one-size-fits-all approach to niche EHRs. Peripheral players providing ancillary care services are becoming technology aware and, in turn, attracting vendor attention. Most of these are small-to-mid size companies that are apt acquisition target for the incumbent behemoths. Examples:
Quantified Self Data Aggregators
General trend of quantified self-help tools is giving way to offerings that aggregate the data from multiple such tools. That’s right. Not the sensor-based devices, but aggregators of those devices. These solutions that aim to collate multiple self-quantification devices (like fitbit, zeo, fuelband etc.) are emerging from the unexplored abyss of Health 2.0. Examples: Sandalbay Life, TicTrac, Paco.
Still formulating overarching thoughts. Patterns like ‘referral management’ are emerging, so stay tuned. Meanwhile, enjoy this informal list of related companies on Multiplyd wiki.