In 1983, the IBM PC XT debuted with 128K of RAM and a 10MB hard disk. In that same year, the first mobile phone debuted weighing about 2.5 pounds and with a $4,000 price tag. Fast forward to today and the average person unlocks their smartphone 76-80 times a day and relies on it for every aspect of their lives. These amazing pieces of hardware are millions of times more capable than all of NASA's computing power in the 1960s. Now that we have a supercomputer that never leaves people's sides, maybe it's time that we do some more innovation and see how that device can be used for "mobile health".
IBM Watson Health has formed a medical imaging collaborative with more than 15 leading healthcare organizations. The goal: To take on some of the most deadly diseases. The collaborative, which includes health systems, academic medical centers, ambulatory radiology providers and imaging technology companies, aims to help doctors address breast, lung, and other cancers; diabetes; eye health; brain disease; and heart disease and related conditions, such as stroke. Watson will mine insights from what IBM calls previously invisible unstructured imaging data and combine it with a broad variety of data from other sources, such as data from electronic health records, radiology and pathology reports, lab results, doctors' progress notes, medical journals, clinical care guidelines and published outcomes studies. As the work of the collaborative evolves, Watson's rationale and insights will evolve, informed by the latest combined thinking of the participating organizations.
Apple is edging its way a little further into health care with the release of new iPhone apps that patients can use to manage their own medical conditions -- from diabetes to pregnancy and even depression. While there are hundreds of health-related apps on the market, Apple wants to put its stamp on a new ecosystem of treatment programs. Rather than build the apps itself, the tech giant developed a set of software tools and templates, called "CareKit," that health-care groups and health-tech startups can use to create their own programs. Apple says it wanted to help developers build easy-to-use apps for patients to record symptoms, get useful information, track their progress and even send reports to a doctor. Experts say the CareKit program could help bring standards to a relatively new and unruly industry, while giving Apple a toehold in the growing health-tech market.
It is impossible to discuss intelligently an episodic, timeoriented method for refinement of a skeletal treatment plan without a proper language for representing time-oriented concepts. Such a temporally oriented language is also necessary for annotation of a clinical guideline's intentions, since the very nature of clinical guidelines and protocols is to recommend actions and follow their results over time. Finally, reasoning about possible revision strategies during execution of a plan over any significant time period requires abstraction of time-stamped data into more manageable, higher-level concepts. Thus, before discussing the representation of clinical-guideline plans, intentions, and potential revisions, we need to examine briefly what the requirements for such a time-oriented language are, and what tools we have for performing the task implied by such representations, that is, the abstraction of data over time. This brief presentation will facilitate the rest of the discussion.
Electronic health records (EHR) data provide a cost and time-effective opportunity to conduct cohort studies of the effects of multiple time-point interventions in the diverse patient population found in real-world clinical settings. Because the computational cost of analyzing EHR data at daily (or more granular) scale can be quite high, a pragmatic approach has been to partition the follow-up into coarser intervals of pre-specified length. Current guidelines suggest employing a 'small' interval, but the feasibility and practical impact of this recommendation has not been evaluated and no formal methodology to inform this choice has been developed. We start filling these gaps by leveraging large-scale EHR data from a diabetes study to develop and illustrate a fast and scalable targeted learning approach that allows to follow the current recommendation and study its practical impact on inference. More specifically, we map daily EHR data into four analytic datasets using 90, 30, 15 and 5-day intervals. We apply a semi-parametric and doubly robust estimation approach, the longitudinal TMLE, to estimate the causal effects of four dynamic treatment rules with each dataset, and compare the resulting inferences. To overcome the computational challenges presented by the size of these data, we propose a novel TMLE implementation, the 'long-format TMLE', and rely on the latest advances in scalable data-adaptive machine-learning software, xgboost and h2o, for estimation of the TMLE nuisance parameters.