Toward a Learning Health-care System – Knowledge Delivery at the Point of Care Empowered by Big Data and NLP

Abstract

The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

Context Aware SmartHealth Cloud Platform for Medical Diagnostics

“…It acquires the smartness of unsupervised learning while at the same time it keeps it under supervision of qualified doctors that would assist in complicated diagnostic case and continuously update the www.ijacsa.thesai.org knowledgebase not compromising on the risk associated. This SmartHealth cloud platform is a very comprehensive and complete visualization of our future vision of LHS [17]. Currently, it lacks actual implementation due to limited resources and time.…”

“…LHS [17] is in its development phase with a lot to compute and found extendable. Based on the thematic taxonomy in Tables I, II and III the optimal diagnostics system for diabetics patients those are threatened by underlying diseases is yet a problem to be achieved with many challenges.…”

A new approach to clinical research: Integrating clinical care, quality reporting, and research using a wound care network‐based learning healthcare system

“…The Research Network of the Patient‐Centered Outcomes Research Institute (PCORI) is among the most important LHS enterprises to date, with 11 clinical data research networks (CDRNs) and 18 patient‐driven research networks that provide EHR data analysis on common and rare conditions and genetic disorders . PCORI denied the application of one of our authors (CEF) to support an already established CDRN tracking DFUs and unusual leg ulcers (e.g., sickle cell anemia ulcers), because [DFUs and nonhealing wounds] were “not a healthcare priority.” The outlook for federal funding sufficient to implement a wound care research network is bleak, despite the staggering costs associated with wounds.…”

“…At the core of the paradigm shift towards value‐based and patient‐centered care is the Learning Healthcare System (LHS), a concept introduced in 2007 by the Health and Medicine Division (HMD), when it was then known as the Institute of Medicine, that integrates clinical care, patient participation, quality improvement (QI), and comparative effectiveness research (CER). In a LHS, knowledge is obtained continuously through routine clinical documentation at the point of care (POC) and turned into guidance through clinical decision support (CDS), with a resulting vast repository of data on treatment effectiveness to enhance RCTs and evidence‐based medicine . The transition of the healthcare system from volume‐based reimbursement to one based on value provides an opportunity to advance wound care research.…”

Using Electronic Health Records To Generate Phenotypes For Research

“…Early data suggest these additional incentives and regulations would positively affect quality of care (Buntin, Burke, Hoaglin, & Blumenthal, ), but early post‐incentive analysis is inconclusive (Enriquez et al., ; Kern, Edwards, Kaushal, & HITEC Investigators, ). Nevertheless, it is ultimately anticipated that the adoption of EHRs and meaningful use functions will spur the evolution of learning health care systems that link and leverage patient and population data to provide an up‐to‐date and comprehensive knowledge base that can be readily accessed at the point of clinical care for treatment and disease prevention (Breitenstein, Liu, Maxwell, Pathak, & Zhang, ; Flum et al., ; Kaggal et al., ; Smith, Saunders, Stuckhardt, & McGinnis, ).…”