Patterns of red blood cell utilization: Harnessing electronic health records data from the Information Standard for Blood and Transplant (ISBT) 128 system within the Biologics Effectiveness and Safety (BEST) initiative.

BACKGROUND: Current hemovigilance methods generally rely on survey data or administrative claims data utilizing billing and revenue codes, each of which has limitations. We used electronic health records (EHR) linked to blood bank data to comprehensively characterize red blood cell (RBC) utilization patterns and trends in three healthcare systems participating in the U.S. Food and Drug Administration Center for Biologics Evaluation and Research Biologics Effectiveness and Safety (BEST) initiative. METHODS: We used Information Standard for Blood and Transplant (ISBT) 128 codes linked to EHR from three healthcare systems data sources to identify and quantify RBC-transfused individuals, RBC transfusion episodes, transfused RBC units, and processing methods per year during 2012-2018. RESULTS: There were 577,822 RBC units transfused among 112,705 patients comprising 345,373 transfusion episodes between 2012 and 2018. Utilization in terms of RBC units and patients increased slightly in one and decreased slightly in the other two healthcare facilities. About 90% of RBC-transfused patients had 1 (~46%) or 2-5 (~42%)transfusion episodes in 2018. Among the small proportion of patients with ≥12 transfusion episodes per year, approximately 60% of episodes included only one RBC unit. All facilities used leukocyte-reduced RBCs during the study period whereas irradiated RBC utilization patterns differed across facilities. DISCUSSION: ISBT 128 codes and EHRs were used to observe patterns of RBC transfusion and modification methods at the unit level and patient level in three healthcare systems participating in the BEST initiative. This study shows that the ISBT 128 coding system in an EHR environment provides a feasible source for hemovigilance activities.

Enhancing narrative clinical guidance with computer-readable artifacts: Authoring FHIR implementation guides based on WHO recommendations.

INTRODUCTION: Narrative clinical guidelines often contain assumptions, knowledge gaps, and ambiguities that make translation into an electronic computable format difficult. This can lead to divergence in electronic implementations, reducing the usefulness of collected data outside of that implementation setting. This work set out to evolve guidelines-based data dictionaries by mapping to HL7 Fast Health Interoperability Resources (FHIR) and semantic terminology, thus progressing toward machine-readable guidelines that define the minimum data set required to support family planning and sexually transmitted infections. MATERIAL AND METHODS: The data dictionaries were first structured to facilitate mapping to FHIR and semantic terminologies, including ICD-10, SNOMED-CT, LOINC, and RxNorm. FHIR resources and codes were assigned to data dictionary terms. The data dictionary and mappings were used as inputs for a newly developed tool to generate FHIR implementation guides. RESULTS: Implementation guides for core data requirements for family planning and sexually transmitted infections were created. These implementation guides display data dictionary content as FHIR resources and semantic terminology codes. Challenges included the use of a two-dimensional spreadsheet to facilitate mapping, the need to create FHIR profiles and resource extensions, and applying FHIR to a data dictionary that was created with a user interface in mind. CONCLUSIONS: Authoring FHIR implementation guides is a complex and evolving practice, and there are limited examples for this groundbreaking work. Moving toward machine-readable guidelines by mapping to FHIR and semantic terminologies requires a thorough understanding of the context and use of terminology, an applied information model, and other strategies for optimizing the creation and long-term management of implementation guides. Next steps for this work include validation and, eventually, real-world application. The process for creating the data dictionary and for generating implementation guides should also be improved to prepare for this expanding work. FUNDING: This work was supported by the World Health Organization, which also worked as a collaborative partner throughout the study.

Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach.

BACKGROUND: As the most commonly occurring form of mental illness worldwide, depression poses significant health and economic burdens to both the individual and community. Different types of depression pose different levels of risk. Individuals who suffer from mild forms of depression may recover without any assistance or be effectively managed by primary care or family practitioners. However, other forms of depression are far more severe and require advanced care by certified mental health providers. However, identifying cases of depression that require advanced care may be challenging to primary care providers and health care team members whose skill sets run broad rather than deep. OBJECTIVE: This study aimed to leverage a comprehensive range of patient-level diagnostic, behavioral, and demographic data, as well as past visit history data from a statewide health information exchange to build decision models capable of predicting the need of advanced care for depression across patients presenting at Eskenazi Health, the public safety net health system for Marion County, Indianapolis, Indiana. METHODS: Patient-level diagnostic, behavioral, demographic, and past visit history data extracted from structured datasets were merged with outcome variables extracted from unstructured free-text datasets and were used to train random forest decision models that predicted the need of advanced care for depression across (1) the overall patient population and (2) various subsets of patients at higher risk for depression-related adverse events; patients with a past diagnosis of depression; patients with a Charlson comorbidity index of ≥1; patients with a Charlson comorbidity index of ≥2; and all unique patients identified across the 3 above-mentioned high-risk groups. RESULTS: The overall patient population consisted of 84,317 adult (aged ≥18 years) patients. A total of 6992 (8.29%) of these patients were in need of advanced care for depression. Decision models for high-risk patient groups yielded area under the curve (AUC) scores between 86.31% and 94.43%. The decision model for the overall patient population yielded a comparatively lower AUC score of 78.87%. The variance of optimal sensitivity and specificity for all decision models, as identified using Youden J Index, is as follows: sensitivity=68.79% to 83.91% and specificity=76.03% to 92.18%. CONCLUSIONS: This study demonstrates the ability to automate screening for patients in need of advanced care for depression across (1) an overall patient population or (2) various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors, and past visit history. Furthermore, these results show considerable potential to enable preventative care and can be easily integrated into existing clinical workflows to improve access to wraparound health care services.

Enabling Better Interoperability for HealthCare: Lessons in Developing a Standards Based Application Programing Interface for Electronic Medical Record Systems.

We sought to enable better interoperability and easy adoption of healthcare applications by developing a standardized domain independent Application Programming Interface (API) for an Electronic Medical Record (EMR) system. We leveraged the modular architecture of the Open Medical Record System (OpenMRS) to build a Fast Healthcare Interoperability Resources (FHIR) based add-on module that could consume FHIR resources and requests made on OpenMRS. The OpenMRS FHIR module supports a subset of FHIR resources that could be used to interact with clinical data persisted in OpenMRS. We demonstrate the ease of connecting healthcare applications using the FHIR API by integrating a third party Substitutable Medical Apps & Reusable Technology (SMART) application with OpenMRS via FHIR. The OpenMRS FHIR module is an optional component of the OpenMRS platform. The FHIR API significantly reduces the effort required to implement OpenMRS by preventing developers from having to learn or work with a domain specific OpenMRS API. We propose an integration pathway where the domain specific legacy OpenMRS API is gradually retired in favor of the new FHIR API, which would be integrated into the core OpenMRS platform. Our efforts indicate that a domain independent API is a reality for any EMR system. These efforts demonstrate the adoption of an emerging FHIR standard that is seen as a replacement for both Health Level 7 (HL7) Version 2 and Version 3. We propose a gradual integration approach where our FHIR API becomes the preferred method for communicating with the OpenMRS platform.

Ontologies Applied in Clinical Decision Support System Rules: Systematic Review.

BACKGROUND: Clinical decision support systems (CDSSs) are important for the quality and safety of health care delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused. OBJECTIVE: Ontologies have the potential to promote the reuse of CDSS rules. Therefore, we systematically screened the literature to elaborate on the current status of ontologies applied in CDSS rules, such as rule management, which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate, and maintenance, which updates CDSS rules. Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules. METHODS: The literature search was focused on the intersection of ontologies; clinical decision support; and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database. Grounded theory and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines were followed. One author initiated the screening and literature review, while 2 authors validated the processes and results independently. The inclusion and exclusion criteria were developed and refined iteratively. RESULTS: CDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules. CONCLUSIONS: Ontologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the reusability and interoperability of CDSS rules. This review identified and described the ontologies that, despite their limitations, enable Semantic Web technologies and their applications in CDSS rules.

Keyphrase Identification Using Minimal Labeled Data with Hierarchical Context and Transfer Learning.

Interoperable clinical decision support system (CDSS) rules provide a pathway to interoperability, a well-recognized challenge in health information technology. Building an ontology facilitates creating interoperable CDSS rules, which can be achieved by identifying the keyphrases (KP) from the existing literature. However, KP identification for data labeling requires human expertise, consensus, and contextual understanding. This paper aims to present a semi-supervised KP identification framework using minimal labeled data based on hierarchical attention over the documents and domain adaptation. Our method outperforms the prior neural architectures by learning through synthetic labels for initial training, document-level contextual learning, language modeling, and fine-tuning with limited gold standard label data. To the best of our knowledge, this is the first functional framework for the CDSS sub-domain to identify KPs, which is trained on limited labeled data. It contributes to the general natural language processing (NLP) architectures in areas such as clinical NLP, where manual data labeling is challenging, and light-weighted deep learning models play a role in real-time KP identification as a complementary approach to human experts' effort.

A Systematic Approach to Configuring MetaMap for Optimal Performance.

BACKGROUND: MetaMap is a valuable tool for processing biomedical texts to identify concepts. Although MetaMap is highly configurative, configuration decisions are not straightforward. OBJECTIVE: To develop a systematic, data-driven methodology for configuring MetaMap for optimal performance. METHODS: MetaMap, the word2vec model, and the phrase model were used to build a pipeline. For unsupervised training, the phrase and word2vec models used abstracts related to clinical decision support as input. During testing, MetaMap was configured with the default option, one behavior option, and two behavior options. For each configuration, cosine and soft cosine similarity scores between identified entities and gold-standard terms were computed for 40 annotated abstracts (422 sentences). The similarity scores were used to calculate and compare the overall percentages of exact matches, similar matches, and missing gold-standard terms among the abstracts for each configuration. The results were manually spot-checked. The precision, recall, and F-measure (ß =1) were calculated. RESULTS: The percentages of exact matches and missing gold-standard terms were 0.6-0.79 and 0.09-0.3 for one behavior option, and 0.56-0.8 and 0.09-0.3 for two behavior options, respectively. The percentages of exact matches and missing terms for soft cosine similarity scores exceeded those for cosine similarity scores. The average precision, recall, and F-measure were 0.59, 0.82, and 0.68 for exact matches, and 1.00, 0.53, and 0.69 for missing terms, respectively. CONCLUSION: We demonstrated a systematic approach that provides objective and accurate evidence guiding MetaMap configurations for optimizing performance. Combining objective evidence and the current practice of using principles, experience, and intuitions outperforms a single strategy in MetaMap configurations. Our methodology, reference codes, measurements, results, and workflow are valuable references for optimizing and configuring MetaMap.

Information discovery on electronic health records using authority flow techniques.

BACKGROUND: As the use of electronic health records (EHRs) becomes more widespread, so does the need to search and provide effective information discovery within them. Querying by keyword has emerged as one of the most effective paradigms for searching. Most work in this area is based on traditional Information Retrieval (IR) techniques, where each document is compared individually against the query. We compare the effectiveness of two fundamentally different techniques for keyword search of EHRs. METHODS: We built two ranking systems. The traditional BM25 system exploits the EHRs' content without regard to association among entities within. The Clinical ObjectRank (CO) system exploits the entities' associations in EHRs using an authority-flow algorithm to discover the most relevant entities. BM25 and CO were deployed on an EHR dataset of the cardiovascular division of Miami Children's Hospital. Using sequences of keywords as queries, sensitivity and specificity were measured by two physicians for a set of 11 queries related to congenital cardiac disease. RESULTS: Our pilot evaluation showed that CO outperforms BM25 in terms of sensitivity (65% vs. 38%) by 71% on average, while maintaining the specificity (64% vs. 61%). The evaluation was done by two physicians. CONCLUSIONS: Authority-flow techniques can greatly improve the detection of relevant information in EHRs and hence deserve further study.

Experience implementing electronic health records in three East African countries.

INTRODUCTION: Efficient use of health care resources in low-income countries by providers and local and national managers requires timely access to patient data. OBJECTIVE: To implement electronic health records (EHRs) in HIV clinics in Kenya, Tanzania, and Uganda. RESULTS: We initially developed and implemented an EHR in Kenya through a mature academic partnership. The EHR was then implemented in six HIV clinics in Tanzania and Uganda in collaboration with their National AIDS Control Programmes. All implementations were successful, but the system's use and sustainability varied depending on who controlled clinic funding. CONCLUSIONS: Successful EHR use and sustainability were enhanced by local control of funds, academic partnerships (mainly by leveraging research funds), and in-country technology support.

Envisioning health equity for American Indian/Alaska Natives: a unique HIT opportunity.

The Indian Health Service provides care to remote and under-resourced communities in the United States. American Indian/Alaska Native patients have some of the highest morbidity and mortality among any ethnic group in the United States. Starting in the 1980s, the IHS implemented the Resource and Patient Management System health information technology (HIT) platform to improve efficiency and quality to address these disparities. The IHS is currently assessing the Resource and Patient Management System to ensure that changing health information needs are met. HIT assessments have traditionally focused on cost, reimbursement opportunities, infrastructure, required or desired functionality, and the ability to meet provider needs. Little information exists on frameworks that assess HIT legacy systems to determine solutions for an integrated rural healthcare system whose end goal is health equity. This search for a next-generation HIT solution for a historically underserved population presents a unique opportunity to envision and redefine HIT that supports health equity as its core mission.

Pediatric decision support using adapted Arden Syntax.

BACKGROUND: Pediatric guidelines based care is often overlooked because of the constraints of a typical office visit and the sheer number of guidelines that may exist for a patient's visit. In response to this problem, in 2004 we developed a pediatric computer based clinical decision support system using Arden Syntax medical logic modules (MLM). METHODS: The Child Health Improvement through Computer Automation system (CHICA) screens patient families in the waiting room and alerts the physician in the exam room. Here we describe adaptation of Arden Syntax to support production and consumption of patient specific tailored documents for every clinical encounter in CHICA and describe the experiments that demonstrate the effectiveness of this system. RESULTS: As of this writing CHICA has served over 44,000 patients at 7 pediatric clinics in our healthcare system in the last decade and its MLMs have been fired 6182,700 times in "produce" and 5334,021 times in "consume" mode. It has run continuously for over 10 years and has been used by 755 physicians, residents, fellows, nurse practitioners, nurses and clinical staff. There are 429 MLMs implemented in CHICA, using the Arden Syntax standard. Studies of CHICA's effectiveness include several published randomized controlled trials. CONCLUSIONS: Our results show that the Arden Syntax standard provided us with an effective way to represent pediatric guidelines for use in routine care. We only required minor modifications to the standard to support our clinical workflow. Additionally, Arden Syntax implementation in CHICA facilitated the study of many pediatric guidelines in real clinical environments.

Experience in implementing the OpenMRS medical record system to support HIV treatment in Rwanda.

The challenge of scaling up HIV treatment in Africa has led to a new emphasis on improving health systems in impoverished areas. One aspect of this is the development and deployment of electronic medical record systems to support HIV and TB treatment. In this paper we describe the design and implementation of a new medical record architecture to support an HIV treatment program in rural Rwanda. The architecture is called OpenMRS and it has been developed to address the problem of configuring EMR systems to suit new sites, languages and diseases. OpenMRS uses a data dictionary called the concept dictionary to represent all the possible data items that can be collected. This allows new items to be added to the system by non-programmers. In addition, there are form creation tools that use drag and drop web technologies to simplify form construction. The OpenMRS system was first implemented in Kenya in February 2006 and then in Rwanda in August 2006. The system is now functioning well and we are developing extensions to improve the support for the clinic. These include improved, easy to use reporting tools, support for additional clinical problems including nutrition and child health, better database synchronization tools, and modules to collect laboratory data and support the pharmacy. The system is also in use in South Africa and Lesotho and is being deployed in Tanzania and Uganda.

The AMPATH medical record system: creating, implementing, and sustaining an electronic medical record system to support HIV/AIDS care in western Kenya.

Providing high-quality HIV/AIDS care requires high-quality, accessible data on individual patients and visits. These data can also drive strategic decision-making by health systems, national programs, and funding agencies. One major obstacle to HIV/AIDS care in developing countries is lack of electronic medical record systems (EMRs) to collect, manage, and report clinical data. In 2001, we implemented a simple primary care EMR at a rural health centre in western Kenya. This EMR evolved into a comprehensive, scalable system serving 19 urban and rural health centres. To date, the AMPATH Medical Record System contains 10 million observations from 400,000 visit records on 45,000 patients. Critical components include paper encounter forms for adults and children, technicians entering/managing data, and modules for patient registration, scheduling, encounters, clinical observations, setting user privileges, and a concept dictionary. Key outputs include patient summaries, care reminders, and reports for program management, operating ancillary services (e.g., tracing patients who fail to return for appointments), strategic planning (e.g., hiring health care providers and staff), reports to national AIDS programs and funding agencies, and research.

An Incremental Adoption Pathway for Developing Precision Medicine Based Healthcare Infrastructure for Underserved Settings.

Recent focus on Precision medicine (PM) has led to a flurry of research activities across the developed world. But how can understaffed and underfunded health care systems in the US and elsewhere evolve to adapt PM to address pressing healthcare needs? We offer guidance on a wide range of sources of healthcare data / knowledge as well as other infrastructure / tools that could inform PM initiatives, and may serve as low hanging fruit easily adapted on the incremental pathway towards a PM based healthcare system. Using these resources and tools, we propose an incremental adoption pathway to inform implementers working in underserved communities around the world on how they should position themselves to gradually embrace the concepts of PM with minimal interruption to existing care delivery.

The influence of information technology on patient-physician relationships.

Interpersonal relationships and information are intertwined as essential cornerstones of health care. Although information technology (IT) has done much to advance medicine, we are not even close to realizing its full potential. Indeed, issues related to mismanaging health information often undermine relationship-centered care. Information technology must be implemented in ways that preserve and uplift relationships in care, while accommodating major deficiencies in managing information and making medical decisions. Increased collaboration between experts in IT and relationship-centered care is needed, along with inclusion of relationship-based measures in informatics research.

Implementing electronic medical record systems in developing countries.

The developing world faces a series of health crises including HIV/AIDS and tuberculosis that threaten the lives of millions of people. Lack of infrastructure and trained, experienced staff are considered important barriers to scaling up treatment for these diseases. In this paper we explain why information systems are important in many healthcare projects in the developing world. We discuss pilot projects demonstrating that such systems are possible and can expand to manage hundreds of thousands of patients. We also pass on the most important practical lessons in design and implementation from our experience in doing this work. Finally, we discuss the importance of collaboration between projects in the development of electronic medical record systems rather than reinventing systems in isolation, and the use of open standards and open source software.

Towards Standardized Patient Data Exchange: Integrating a FHIR Based API for the Open Medical Record System.

Interoperability is essential to address limitations caused by the ad hoc implementation of clinical information systems and the distributed nature of modern medical care. The HL7 V2 and V3 standards have played a significant role in ensuring interoperability for healthcare. FHIR is a next generation standard created to address fundamental limitations in HL7 V2 and V3. FHIR is particularly relevant to OpenMRS, an Open Source Medical Record System widely used across emerging economies. FHIR has the potential to allow OpenMRS to move away from a bespoke, application specific API to a standards based API. We describe efforts to design and implement a FHIR based API for the OpenMRS platform. Lessons learned from this effort were used to define long term plans to transition from the legacy OpenMRS API to a FHIR based API that greatly reduces the learning curve for developers and helps enhance adhernce to standards.

Do clinical decision-support reminders for medical providers improve isoniazid preventative therapy prescription rates among HIV-positive adults? Study protocol for a randomized controlled trial.

BACKGROUND: This document describes a research protocol for a study designed to estimate the impact of implementing a reminder system for medical providers on the use of isoniazid preventative therapy (IPT) for adults living with HIV in western Kenya. People living with HIV have a 5% to 10% annual risk of developing active tuberculosis (TB) once infected with TB bacilli, compared to a 5% lifetime risk in HIV-negative people with latent TB infection. Moreover, people living with HIV have a 20-fold higher risk of dying from TB. A growing body of literature suggests that IPT reduces overall TB incidence and is therefore of considerable benefit to patients and the larger community. However, in 2009, of the estimated 33 million people living with HIV, only 1.7 million (5%) were screened for TB, and about 85,000 (0.2%) were offered IPT. METHODS/DESIGN: This study will examine the use of clinical decision-support reminders to improve rates of initiation of preventative treatment in a TB/HIV co-morbid population living in a TB endemic area. This will be a pragmatic, parallel-group, cluster-randomized superiority trial with a 1:1 allocation to treatment ratio. For the trial, 20 public medical facilities that use clinical summary sheets generated from an electronic medical records system will participate as clusters. All HIV-positive adult patients who complete an initial encounter at a study cluster and at least one return encounter during the study period will be included in the study cohort. The primary endpoint will be IPT prescription at 3 months post the initial encounter. We will conduct both individual-level and cluster-level analyses. Due to the nature of the intervention, the trial will not be blinded. This study will contribute to the growing evidence base for the use of electronic health interventions in low-resource settings to promote high-quality clinical care, health system optimization and positive patient outcomes. Trial registration ClinicalTrials.gov NCT01934309, registered 29 August 2013.

Child Health Improvement through Computer Automation: the CHICA system.

Clinical guidelines are prevalent but frequently not used. Computer reminder systems can improve adherence to guidelines but have not been widely adopted. We present a computer-based decision support system that combines these elements: 1) pediatric preventive care guidelines encoded in Arden Syntax; 2) a dynamic, scannable paper user interface; and 3) a HL7-compliant interface to existing electronic medical record systems. The result is a system that both delivers "just in time" patient-relevant guidelines to physicians during the clinical encounter and accurately captures structured data from all who interact with the system. The system performs these tasks while remaining sensitive to the workflow constraints of a busy outpatient pediatric practice.