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This paper offers a case study to demonstrate how a complex scoring model tool called CNS-TAP, originally created by a neuro-oncology team at one institution, was upgraded and made accessible to a wider audience. In the Results and Discussion, many issues of web app design, development, and sustainability are covered. Overall, we chart a path to expand access to many unique software tools created and needed by today’s medical specialists.

We report the methods, results, and some implications of an experiment to provide API access to reorganized, re-represented content from a current U.S. Food and Drug Administration (FDA) Structured Product Label (SPL). Using a question-and-answer pattern and current technical approach, we demonstrate that prescribing information in SPLs can be converted to a more granular, highly computable, API-accessible format. Our results suggest that computable prescribing information can be systematically published in drug product-specific modules, deployed and widely distributed via APIs, and integrated with downstream client apps to answer key questions about medications and compute patient-specific advice.

A coarse classification of medications into two risk categories, one for high-risk medications and one for all others, allows people to focus safety improvement work on medications that carry the highest risks of harm. However, such coarse categorization does not distinguish the relative risk of harm for the majority of medications. To begin to develop a more fine-grained measurement scale for the relative risk of harm spanning many medications, we performed an experiment with 18 practicing pharmacists. Each pharmacist-participant made 210 paired comparisons of 21 commonly prescribed medications to reveal a subjective scale of perceived medication worrisomeness (PMW). Statistical analyses of their collective judgments of medication pairs differentiated five levels of PMW. This study illuminates one path towards a fine-grained medication risk scale based on PMW. It also shows how the method of paired comparisons can be used to remotely crowdsource expert knowledge in support of learning health systems.

To improve care coordination, forestall emergencies, and save costs, there is a growing movement to have patients at home habitually collect and provide data about their health status and related experiences to health care providers. One aspect of this movement involves the recurring use of reliable and valid questionnaires to collect and share patient reported outcomes (PRO) data. However, studies indicate that health care providers have a host of legitimate concerns about the systematic and routine collection, sharing, and use of PRO data. To explore these provider-specific concerns further, we developed a configurable prototype software application for use by providers and their patients called the PRO Data Dashboard (PDD). Using the PDD along with several written clinical case scenarios, we conducted focus groups with a diverse group of oncology care providers to generate findings relevant to the design, configuration, and use of PRO-based tools. We organized our findings into three categories: scope of use of PRO tools, configuration of PRO tools, and clinician user experience. We conclude that providers require fine-grained control over the configuration of PRO data collection and sharing features in e-Health applications.

Many obstacles must be overcome to generate new biomedical knowledge from real-world data and then directly apply the newly generated knowledge for decision support. Attempts to bridge the processes of data analysis and technical implementation of analytic results reveal a number of gaps. As one example, the knowledge format used to communicate results from data analysis often differs from the knowledge format required by systems to compute advice. We asked whether a shared format could be used by both processes. To address this question, we developed a data-to-advice pipeline called ScriptNumerate. ScriptNumerate analyzes historical e-prescription data and communicates its results in a compound digital object format. ScriptNumerate then uses these same compound digital objects to compute its advice about whether new e-prescriptions have common, rare, or unprecedented instructions. ScriptNumerate demonstrates that data-to-advice pipelines are feasible. In the future, data-to-advice pipelines similar to ScriptNumerate may help support Learning Health Systems.

The Knowledge Grid (KGrid) is a research and development program toward infrastructure capable of greatly decreasing latency between the publication of new biomedical knowledge and its widespread uptake into practice. KGrid comprises digital knowledge objects, an online Library to store them, and an Activator that uses them to provide Knowledge-as-a-Service (KaaS). KGrid's Activator enables computable biomedical knowledge, held in knowledge objects, to be rapidly deployed at Internet-scale in cloud computing environments for improved health. Here we present the Activator, its system architecture and primary functions.

Throughout the world, biomedical knowledge is routinely generated and shared through primary and secondary scientific publications. However, there is too much latency between publication of knowledge and its routine use in practice. To address this latency, what is actionable in scientific publications can be encoded to make it computable. We have created a purpose-built digital library platform to hold, manage, and share actionable, computable knowledge for health called the Knowledge Grid Library. Here we present it with its system architecture.

This paper reports a CMOS-integrated MEMS microgravity accelerometer that implements a novel four-quadrant symmetric device structure with a full-bridge capacitive output. A robust silicon-on-insulator (SOI) MEMS process integrated with a standard CMOS wafer offers a high-sensitivity, low-noise and low-drift accelerometer that achieves measurement of acceleration in micro-g level. The accelerometers are fabricated and packaged on wafer-level with a silicon cap wafer. The measured sensitivity of the CMOS-integrated accelerometer is at 1.8 V/g with a power dissipation of 6mW at 3 V.