CBK model composition using paired web services and executable functions: A demonstration for individualizing preventive services.

Introduction: Learning health systems are challenged to combine computable biomedical knowledge (CBK) models. Using common technical capabilities of the World Wide Web (WWW), digital objects called Knowledge Objects, and a new pattern of activating CBK models brought forth here, we aim to show that it is possible to compose CBK models in more highly standardized and potentially easier, more useful ways. Methods: Using previously specified compound digital objects called Knowledge Objects, CBK models are packaged with metadata, API descriptions, and runtime requirements. Using open-source runtimes and a tool we developed called the KGrid Activator, CBK models can be instantiated inside runtimes and made accessible via RESTful APIs by the KGrid Activator. The KGrid Activator then serves as a gateway and provides a means to interconnect CBK model outputs and inputs, thereby establishing a CBK model composition method. Results: To demonstrate our model composition method, we developed a complex composite CBK model from 42 CBK submodels. The resulting model called CM-IPP is used to compute life-gain estimates for individuals based their personal characteristics. Our result is an externalized, highly modularized CM-IPP implementation that can be distributed and made runnable in any common server environment. Discussion: CBK model composition using compound digital objects and the distributed computing technologies is feasible. Our method of model composition might be usefully extended to bring about large ecosystems of distinct CBK models that can be fitted and re-fitted in various ways to form new composites. Remaining challenges related to the design of composite models include identifying appropriate model boundaries and organizing submodels to separate computational concerns while optimizing reuse potential. Conclusion: Learning health systems need methods for combining CBK models from a variety of sources to create more complex and useful composite models. It is feasible to leverage Knowledge Objects and common API methods in combination to compose CBK models into complex composite models.

Expanding Access to CNS-TAP: Design, Development, and Initial Use of a Complex Precision Health Specialty Web App for Neuro-Oncology.

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.

Engaging Pharmacists to Crowdsource a Fine-grained Medication Risk Scale: An Initial Measurement Study Using Paired Comparisons of Medications.

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.

ScriptNumerate: A Data-to-Advice Pipeline using Compound Digital Objects to Increase the Interoperability of Computable Biomedical Knowledge.

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.

Architecture and Initial Development of a Knowledge-as-a-Service Activator for Computable Knowledge Objects for Health.

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.

Architecture and Initial Development of a Digital Library Platform for Computable Knowledge Objects for Health.

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.