Biomedical informatics and outcomes research: enabling knowledge-driven health care

Bridging the Gap: Exploring Bronchopulmonary Dysplasia through the Lens of Biomedical Informatics

Bronchopulmonary dysplasia (BPD), a chronic lung disease predominantly affecting premature infants, poses substantial clinical challenges. This review delves into the promise of biomedical informatics (BMI) in reshaping BPD research and care. We commence by highlighting the escalating prevalence and healthcare impact of BPD, emphasizing the necessity for innovative strategies to comprehend its intricate nature. To this end, we introduce BMI as a potent toolset adept at managing and analyzing extensive, diverse biomedical data. The challenges intrinsic to BPD research are addressed, underscoring the inadequacies of conventional approaches and the compelling need for data-driven solutions. We subsequently explore how BMI can revolutionize BPD research, encompassing genomics and personalized medicine to reveal potential biomarkers and individualized treatment strategies. Predictive analytics emerges as a pivotal facet of BMI, enabling early diagnosis and risk assessment for timely interventions. Moreover, we examine how mobile health technologies facilitate real-time monitoring and enhance patient engagement, ultimately refining BPD management. Ethical and legal considerations surrounding BMI implementation in BPD research are discussed, accentuating issues of privacy, data security, and informed consent. In summation, this review highlights BMI's transformative potential in advancing BPD research, addressing challenges, and opening avenues for personalized medicine and predictive analytics.

A Novel Machine Learning Model Developed to Assist in Patient Selection for Outpatient Total Shoulder Arthroplasty

INTRODUCTION

Patient selection for outpatient total shoulder arthroplasty (TSA) is important to optimizing patient outcomes. This study aims to develop a machine learning tool that may aid in patient selection for outpatient total should arthroplasty based on medical comorbidities and demographic factors.

METHODS

Patients undergoing elective TSA from 2011 to 2016 in the American College of Surgeons National Surgical Quality Improvement Program were queried. A random forest machine learning model was used to predict which patients had a length of stay of 1 day or less (short stay). A multivariable logistic regression was then used to identify which variables were significantly correlated with a short or long stay.

RESULTS

From 2011 to 2016, 4,500 patients were identified as having undergone elective TSA and having the necessary predictive features and outcomes recorded. The machine learning model was able to successfully identify short stay patients, producing an area under the receiver operator curve of 0.77. The multivariate logistic regression identified numerous variables associated with a short stay including age less than 70 years and male sex as well as variables associated with a longer stay including diabetes, chronic obstructive pulmonary disease, and American Society of Anesthesiologists class greater than 2.

CONCLUSIONS

Machine learning may be used to predict which patients are suitable candidates for short stay or outpatient TSA based on their medical comorbidities and demographic profile.

Expanding transplant outcomes research opportunities through the use of a common data model

The volume of solid organ transplant in the United States is increasing, providing improved quality of life and survival for patients with organ failure. The growth of transplant requires a systematized management of transplant outcomes assessment, especially with the movement toward value-based care. However, there are several challenges to analyzing outcomes in the current registry-based, transplant reporting system: (1) longitudinal data points are difficult to capture in outcomes models; (2) data elements are restricted to those that already exist in the registry data; and (3) there is a delay in the release of outcomes report. In this article, we propose an informatics approach to solve these problems by using a "common data model" to integrate disparate data sources, data elements, and temporal data points. Adopting such a framework can enable multicenter outcomes analyses among transplant centers, nationally and internationally.

Brief strategic therapy for obsessive-compulsive disorder: a clinical and research protocol of a one-group observational study

INTRODUCTION

Obsessive-compulsive disorder (OCD) is a disabling psychopathology. The mainstay of treatment includes cognitive-behavioural therapy (CBT) and medication management. However, individual suffering, functional impairments as well as the direct and indirect costs associated with the disease remain substantial. New treatment programmes are necessary and the brief strategic therapy (BST) has recently shown encouraging results in clinical practice but no quantitative study has as yet been conducted.

METHODS AND ANALYSIS

The clinical effectiveness of the OCD-specific BST protocol will be evaluated in a one-group observational study. Participants will be sequentially recruited from a state community psychotherapy clinic in Dublin, Ireland. Outcome measures will be the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) and the Beck Depression Inventory-II (BDI-II). Data will be collected at baseline, at treatment termination and at 3 month follow-up. The statistical significance of the post-treatment effect will be assessed by the paired-sample Student t test, while clinical significance will be evaluated by means of the equivalence testing method, which will be also used to assess the maintenance of effect at follow-up.

ETHICS/DISSEMINATION

The present study is approved by the Hesed House Ethics Board in Dublin. Findings will enhance the evidence-based knowledge about the clinical effectiveness of BST in treating OCD symptoms, prior to assessing its efficacy in a randomised and controlled clinical trial, and will be disseminated through publication in peer-reviewed journals and conference presentations.

A De-identification method for bilingual clinical texts of various note types

De-identification of personal health information is essential in order not to require written patient informed consent. Previous de-identification methods were proposed using natural language processing technology in order to remove the identifiers in clinical narrative text, although these methods only focused on narrative text written in English. In this study, we propose a regular expression-based de-identification method used to address bilingual clinical records written in Korean and English. To develop and validate regular expression rules, we obtained training and validation datasets composed of 6,039 clinical notes of 20 types and 5,000 notes of 33 types, respectively. Fifteen regular expression rules were constructed using the development dataset and those rules achieved 99.87% precision and 96.25% recall for the validation dataset. Our de-identification method successfully removed the identifiers in diverse types of bilingual clinical narrative texts. This method will thus assist physicians to more easily perform retrospective research.

Implementation strategies of Systems Medicine in clinical research and home care for cardiovascular disease patients

Insights from the "-omics" science have recently emphasized the need to implement an overall strategy in medical research. Here, the development of Systems Medicine has been indicated as a potential tool for clinical translation of basic research discoveries. Systems Medicine also gives the opportunity of improving different steps in medical practice, from diagnosis to healthcare management, including clinical research. The development of Systems Medicine is still hampered however by several challenges, the main one being the development of computational tools adequate to record, analyze and share a large amount of disparate data. In addition, available informatics tools appear not yet fully suitable for the challenge because they are not standardized, not universally available, or with ethical/legal concerns. Cardiovascular diseases (CVD) are a very promising area for translating Systems Medicine into clinical practice. By developing clinically applied technologies, the collection and analysis of data may improve CV risk stratification and prediction. Standardized models for data recording and analysis can also greatly broaden data exchange, thus promoting a uniform management of CVD patients also useful for clinical research. This advance however requires a great organizational effort by both physicians and health institutions, as well as the overcoming of ethical problems. This narrative review aims at providing an update on the state-of-art knowledge in the area of Systems Medicine as applied to CVD, focusing on current critical issues, providing a road map for its practical implementation.

Sustainability considerations for health research and analytic data infrastructures

Introduction:

The United States has made recent large investments in creating data infrastructures to support the important goals of patient-centered outcomes research (PCOR) and comparative effectiveness research (CER), with still more investment planned. These initial investments, while critical to the creation of the infrastructures, are not expected to sustain them much beyond the initial development. To provide the maximum benefit, the infrastructures need to be sustained through innovative financing models while providing value to PCOR and CER researchers.

Sustainability Factors:

Based on our experience with creating flexible sustainability strategies (i.e., strategies that are adaptive to the different characteristics and opportunities of a resource or infrastructure), we define specific factors that are important considerations in developing a sustainability strategy. These factors include assets, expansion, complexity, and stakeholders. Each factor is described, with examples of how it is applied. These factors are dimensions of variation in different resources, to which a sustainability strategy should adapt.

Summary Observations:

We also identify specific important considerations for maintaining an infrastructure, so that the long-term intended benefits can be realized. These observations are presented as lessons learned, to be applied to other sustainability efforts. We define the lessons learned, relating them to the defined sustainability factors as interactions between factors.

Conclusion and Next Steps:

Using perspectives and experiences from a diverse group of experts, we define broad characteristics of sustainability strategies and important observations, which can vary for different projects. Other descriptions of adaptive, flexible, and successful models of collaboration between stakeholders and data infrastructures can expand this framework by identifying other factors for sustainability, and give more concrete directions on how sustainability can be best achieved.

Characteristics desired in clinical data warehouse for biomedical research

Objectives

Due to the unique characteristics of clinical data, clinical data warehouses (CDWs) have not been successful so far. Specifically, the use of CDWs for biomedical research has been relatively unsuccessful thus far. The characteristics necessary for the successful implementation and operation of a CDW for biomedical research have not clearly defined yet.

Methods

Three examples of CDWs were reviewed: a multipurpose CDW in a hospital, a CDW for independent multi-institutional research, and a CDW for research use in an institution. After reviewing the three CDW examples, we propose some key characteristics needed in a CDW for biomedical research.

Results

A CDW for research should include an honest broker system and an Institutional Review Board approval interface to comply with governmental regulations. It should also include a simple query interface, an anonymized data review tool, and a data extraction tool. Also, it should be a biomedical research platform for data repository use as well as data analysis.

Conclusions

The proposed characteristics desired in a CDW may have limited transfer value to organizations in other countries. However, these analysis results are still valid in Korea, and we have developed clinical research data warehouse based on these desiderata.

Knowledge management and informatics considerations for comparative effectiveness research: a case-driven exploration

BACKGROUND

As clinical data are increasingly collected and stored electronically, their potential use for comparative effectiveness research (CER) grows. Despite this promise, challenges face those wishing to leverage such data. In this paper we aim to enumerate some of the knowledge management and informatics issues common to such data reuse.

DESIGN

After reviewing the current state of knowledge regarding biomedical informatics challenges and best practices related to CER, we then present 2 research projects at our institution. We analyze these and highlight several common themes and challenges related to the conduct of CER studies. Finally, we represent these emergent themes.

RESULTS

The informatics challenges commonly encountered by those conducting CER studies include issues related to data information and knowledge management (eg, data reuse, data preparation) as well as those related to people and organizational issues (eg, sociotechnical factors and organizational factors). Examples of these are described in further detail and a formal framework for describing these findings is presented.

CONCLUSIONS

Significant challenges face researchers attempting to use often diverse and heterogeneous datasets for CER. These challenges must be understood in order to be dealt with successfully and can often be overcome with the appropriate use of informatics best practices. Many research and policy questions remain to be answered in order to realize the full potential of the increasingly electronic clinical data available for such research.

Advancing user experience research to facilitate and enable patient-centered research: current state and future directions

Human-computer interaction and related areas of user experience (UX) research, such as human factors, workflow evaluation, and data visualization, are thus essential to presenting data in ways that can further the analysis of complex data sets such as those used in patient-centered research. However, a review of available data on the state of UX research as it relates to patient-centered research demonstrates a significant underinvestment and consequently a large gap in knowledge generation. In response, this report explores trends in funding and research productivity focused on UX and patient-centered research and then presents a set of recommendations to advance innovation at this important intersection point. Ultimately, the aim is to catalyze a community-wide dialogue concerning future directions for research and innovation in UX as it applies to patient-centered research.

Computerized provider documentation: findings and implications of a multisite study of clinicians and administrators

Objective

Clinical documentation is central to the medical record and so to a range of healthcare and business processes. As electronic health record adoption expands, computerized provider documentation (CPD) is increasingly the primary means of capturing clinical documentation. Previous CPD studies have focused on particular stakeholder groups and sites, often limiting their scope and conclusions. To address this, we studied multiple stakeholder groups from multiple sites across the USA.

Methods

We conducted 14 focus groups at five Department of Veterans Affairs facilities with 129 participants (54 physicians or practitioners, 34 nurses, and 37 administrators). Investigators qualitatively analyzed resultant transcripts, developed categories linked to the data, and identified emergent themes.

Results

Five major themes related to CPD emerged: communication and coordination; control and limitations in expressivity; information availability and reasoning support; workflow alteration and disruption; and trust and confidence concerns. The results highlight that documentation intertwines tightly with clinical and administrative workflow. Perceptions differed between the three stakeholder groups but remained consistent within groups across facilities.

Conclusions

CPD has dramatically changed documentation processes, impacting clinical understanding, decision-making, and communication across multiple groups. The need for easy and rapid, yet structured and constrained, documentation often conflicts with the need for highly reliable and retrievable information to support clinical reasoning and workflows. Current CPD systems, while better than paper overall, often do not meet the needs of users, partly because they are based on an outdated ‘paper-chart’ paradigm. These findings should inform those implementing CPD systems now and future plans for more effective CPD systems.

Computational challenges and human factors influencing the design and use of clinical research participant eligibility pre-screening tools

Background

Clinical trials are the primary mechanism for advancing clinical care and evidenced-based practice, yet challenges with the recruitment of participants for such trials are widely recognized as a major barrier to these types of studies. Data warehouses (DW) store large amounts of heterogenous clinical data that can be used to enhance recruitment practices, but multiple challenges exist when using a data warehouse for such activities, due to the manner of collection, management, integration, analysis, and dissemination of the data. A critical step in leveraging the DW for recruitment purposes is being able to match trial eligibility criteria to discrete and semi-structured data types in the data warehouse, though trial eligibility criteria tend to be written without concern for their computability. We present the multi-modal evaluation of a web-based tool that can be used for pre-screening patients for clinical trial eligibility and assess the ability of this tool to be practically used for clinical research pre-screening and recruitment.

Methods

The study used a validation study, usability testing, and a heuristic evaluation to evaluate and characterize the operational characteristics of the software as well as human factors affecting its use.

Results

Clinical trials from the Division of Cardiology and the Department of Family Medicine were used for this multi-modal evaluation, which included a validation study, usability study, and a heuristic evaluation. From the results of the validation study, the software demonstrated a positive predictive value (PPV) of 54.12% and 0.7%, respectively, and a negative predictive value (NPV) of 73.3% and 87.5%, respectively, for two types of clinical trials. Heuristic principles concerning error prevention and documentation were characterized as the major usability issues during the heuristic evaluation.

Conclusions

This software is intended to provide an initial list of eligible patients to a clinical study coordinators, which provides a starting point for further eligibility screening by the coordinator. Because this software has a high “rule in” ability, meaning that it is able to remove patients who are not eligible for the study, the use of an automated tool built to leverage an existing enterprise DW can be beneficial to determining eligibility and facilitating clinical trial recruitment through pre-screening. While the results of this study are promising, further refinement and study of this and related approaches to automated eligibility screening, including comparison to other approaches and stakeholder perceptions, are needed and future studies are planned to address these needs.

A surveillance tool to support quality assurance and research in personalized medicine

Developing effective methods to enable the practice of personalized medicine is a national priority for translational science. By leveraging modern genotyping technology and health information technologies, prescribing therapies based on genotype becomes an achievable goal. Within this manuscript, we describe the development, implementation, and piloting of a surveillance tool to assure the quality of clinical decision making in the context of new pharmacogenetic information. The surveillance tool allows a quality assurance (QA) team to review significant genotyping results and deliver focused educational interventions to providers. We report on the first eight patients undergoing genotyping to support antiplatelet therapy selection after drug-eluting stent placement. The collected pilot data supports an informatics approach to QA process management, as our tool delivered actionable patient information. It also enabled providers to tailor antiplatelet therapy to individual patients' genotypes. Our expectation is to continue collecting surveillance reports to perform an in-depth analysis of our tool.

A cognitive task analysis of a visual analytic workflow: Exploring molecular interaction networks in systems biology

BACKGROUND

Bioinformatics visualization tools are often not robust enough to support biomedical specialists’ complex exploratory analyses. Tools need to accommodate the workflows that scientists actually perform for specific translational research questions. To understand and model one of these workflows, we conducted a case-based, cognitive task analysis of a biomedical specialist’s exploratory workflow for the question: What functional interactions among gene products of high throughput expression data suggest previously unknown mechanisms of a disease?

RESULTS

From our cognitive task analysis four complementary representations of the targeted workflow were developed. They include: usage scenarios, flow diagrams, a cognitive task taxonomy, and a mapping between cognitive tasks and user-centered visualization requirements. The representations capture the flows of cognitive tasks that led a biomedical specialist to inferences critical to hypothesizing. We created representations at levels of detail that could strategically guide visualization development, and we confirmed this by making a trial prototype based on user requirements for a small portion of the workflow.

CONCLUSIONS

Our results imply that visualizations should make available to scientific users “bundles of features” consonant with the compositional cognitive tasks purposefully enacted at specific points in the workflow. We also highlight certain aspects of visualizations that: (a) need more built-in flexibility; (b) are critical for negotiating meaning; and (c) are necessary for essential metacognitive support.

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams.