Early experiences with big data at an academic medical center

Translational Bioethics and Health Privacy

Translational bioethics expands the scope of research ethics to include multidisciplinary analyses of the societal implications of new translational science discoveries. Novel health privacy issues are raised by the collection, use, and disclosure of extensive and diverse big data for research on precision medicine. Similar privacy concerns surround the use of artificial intelligence to analyze vast troves of clinical records to improve patient outcomes. Embedding bioethics scholars with translational scientists can improve the technical analyses and timeliness of bioethical inquiries, but they complicate the task of producing independent and rigorous ethical assessments.

The path from big data analytics capabilities to value in hospitals: a scoping review

BACKGROUND

As the uptake of health information technologies increased, most healthcare organizations have become producers of big data. A growing number of hospitals are investing in the development of big data analytics (BDA) capabilities. If the promises associated with these capabilities are high, how hospitals create value from it remains unclear. The present study undertakes a scoping review of existing research on BDA use in hospitals to describe the path from BDA capabilities (BDAC) to value and its associated challenges.

METHODS

This scoping review was conducted following Arksey and O'Malley's 5 stages framework. A systematic search strategy was adopted to identify relevant articles in Scopus and Web of Science. Data charting and extraction were performed following an analytical framework that builds on the resource-based view of the firm to describe the path from BDA capabilities to value in hospitals.

RESULTS

Of 1,478 articles identified, 94 were included. Most of them are experimental research (n=69) published in medical (n=66) or computer science journals (n=28). The main value targets associated with the use of BDA are improving the quality of decision-making (n=56) and driving innovation (n=52) which apply mainly to care (n=67) and administrative (n=48) activities. To reach these targets, hospitals need to adequately combine BDA capabilities and value creation mechanisms (VCM) to enable knowledge generation and drive its assimilation. Benefits are endpoints of the value creation process. They are expected in all articles but realized in a few instances only (n=19).

CONCLUSIONS

This review confirms the value creation potential of BDA solutions in hospitals. It also shows the organizational challenges that prevent hospitals from generating actual benefits from BDAC-building efforts. The configuring of strategies, technologies and organizational capabilities underlying the development of value-creating BDA solutions should become a priority area for research, with focus on the mechanisms that can drive the alignment of BDA and organizational strategies, and the development of organizational capabilities to support knowledge generation and assimilation.

A Roadmap for Automating Lineage Tracing to Aid Automatically Explaining Machine Learning Predictions for Clinical Decision Support

Using machine learning predictive models for clinical decision support has great potential in improving patient outcomes and reducing health care costs. However, most machine learning models are black boxes that do not explain their predictions, thereby forming a barrier to clinical adoption. To overcome this barrier, an automated method was recently developed to provide rule-style explanations of any machine learning model’s predictions on tabular data and to suggest customized interventions. Each explanation delineates the association between a feature value pattern and an outcome value. Although the association and intervention information is useful, the user of the automated explaining function often requires more detailed information to better understand the patient’s situation and to aid in decision making. More specifically, consider a feature value in the explanation that is computed by an aggregation function on the raw data, such as the number of emergency department visits related to asthma that the patient had in the prior 12 months. The user often wants to rapidly drill through to see certain parts of the related raw data that produce the feature value. This task is frequently difficult and time-consuming because the few pieces of related raw data are submerged by many pieces of raw data of the patient that are unrelated to the feature value. To address this issue, this paper outlines an automated lineage tracing approach, which adds automated drill-through capability to the automated explaining function, and provides a roadmap for future research.

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

Background

Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well.

Objective

To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions.

Methods

We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation.

Results

We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years.

Conclusions

Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources.

International Registered Report Identifier (IRRID)

PRR1-10.2196/27065

Big Data, Surveillance Capitalism, and Precision Medicine: Challenges for Privacy

Surveillance capitalism companies, such as Google and Facebook, have substantially increased the amount of information collected, analyzed, and monetized, including health information increasingly used in precision medicine research, thereby presenting great challenges for health privacy.

Informed Consent for Secondary Research under the New NIH Data Sharing Policy

The new NIH data sharing policy, effective January 2023, requires researchers to submit a data management and data sharing plan in their grant application. Expanded data sharing, encouraged by NIH to facilitate secondary research, will require informed consent documents to explain data sharing plans, limitations, and procedures.

Illuminating the dark spaces of healthcare with ambient intelligence

Advances in machine learning and contactless sensors have given rise to ambient intelligence-physical spaces that are sensitive and responsive to the presence of humans. Here we review how this technology could improve our understanding of the metaphorically dark, unobserved spaces of healthcare. In hospital spaces, early applications could soon enable more efficient clinical workflows and improved patient safety in intensive care units and operating rooms. In daily living spaces, ambient intelligence could prolong the independence of older individuals and improve the management of individuals with a chronic disease by understanding everyday behaviour. Similar to other technologies, transformation into clinical applications at scale must overcome challenges such as rigorous clinical validation, appropriate data privacy and model transparency. Thoughtful use of this technology would enable us to understand the complex interplay between the physical environment and health-critical human behaviours.

Open Source Infrastructure for Health Care Data Integration and Machine Learning Analyses

PURPOSE

We have created a cloud-based machine learning system (CLOBNET) that is an open-source, lean infrastructure for electronic health record (EHR) data integration and is capable of extract, transform, and load (ETL) processing. CLOBNET enables comprehensive analysis and visualization of structured EHR data. We demonstrate the utility of CLOBNET by predicting primary therapy outcomes of patients with high-grade serous ovarian cancer (HGSOC) on the basis of EHR data.

MATERIALS AND METHODS

CLOBNET is built using open-source software to make data preprocessing, analysis, and model training user friendly. The source code of CLOBNET is available in GitHub. The HGSOC data set was based on a prospective cohort of 208 patients with HGSOC who were treated at Turku University Hospital, Finland, from 2009 to 2019 for whom comprehensive clinical and EHR data were available.

RESULTS

We trained machine learning (ML) models using clinical data, including a herein developed dissemination score that quantifies the disease burden at the time of diagnosis, to identify patients with progressive disease (PD) or a complete response (CR) on the basis of RECIST (version 1.1). The best performance was achieved with a logistic regression model, which resulted in an area under receiver operating characteristic curve (AUROC) of 0.86, with a specificity of 73% and a sensitivity of 89%, when it classified between patients who experienced PD and CR.

CONCLUSION

We have developed an open-source computational infrastructure, CLOBNET, that enables effective and rapid analysis of EHR and other clinical data. Our results demonstrate that CLOBNET allows predictions to be made on the basis of EHR data to address clinically relevant questions.

Accounting for accountable care: Value-based population health management

Accountable Care Organizations (ACOs) are exemplars of so-called value-based care in the US. In this model, healthcare providers bear the financial risk of their patients' health outcomes: ACOs are rewarded for meeting specific quality and cost-efficiency benchmarks, or penalized if improvements are not demonstrated. While the aim is to make providers more accountable to payers and patients, this is a sea-change in payment and delivery systems, requiring new infrastructures and practices. To manage risk, ACOs employ data-intensive sourcing and big data analytics to identify individuals within their populations and sort them using novel categories, which are then utilized to tailor interventions. The article uses an STS lens to analyze the assemblage involved in the enactment of population health management through practices of data collection, the creation of new metrics and tools for analysis, and novel ways of sorting individuals within populations. The processes and practices of implementing accountability technologies thus produce particular kinds of knowledge and reshape concepts of accountability and care. In the process, account-giving becomes as much a procedural ritual of verification as an accounting for health outcomes.

Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis

Background

Both chronic obstructive pulmonary disease (COPD) and asthma incur heavy health care burdens. To support tailored preventive care for these 2 diseases, predictive modeling is widely used to give warnings and to identify patients for care management. However, 3 gaps exist in current modeling methods owing to rarely factoring in temporal aspects showing trends and early health change: (1) existing models seldom use temporal features and often give late warnings, making care reactive. A health risk is often found at a relatively late stage of declining health, when the risk of a poor outcome is high and resolving the issue is difficult and costly. A typical model predicts patient outcomes in the next 12 months. This often does not warn early enough. If a patient will actually be hospitalized for COPD next week, intervening now could be too late to avoid the hospitalization. If temporal features were used, this patient could potentially be identified a few weeks earlier to institute preventive therapy; (2) existing models often miss many temporal features with high predictive power and have low accuracy. This makes care management enroll many patients not needing it and overlook over half of the patients needing it the most; (3) existing models often give no information on why a patient is at high risk nor about possible interventions to mitigate risk, causing busy care managers to spend more time reviewing charts and to miss suited interventions. Typical automatic explanation methods cannot handle longitudinal attributes and fully address these issues.

Objective

To fill these gaps so that more COPD and asthma patients will receive more appropriate and timely care, we will develop comprehensible data-driven methods to provide accurate early warnings of poor outcomes and to suggest tailored interventions, making care more proactive, efficient, and effective.

Methods

By conducting a secondary data analysis and surveys, the study will: (1) use temporal features to provide accurate early warnings of poor outcomes and assess the potential impact on prediction accuracy, risk warning timeliness, and outcomes; (2) automatically identify actionable temporal risk factors for each patient at high risk for future hospital use and assess the impact on prediction accuracy and outcomes; and (3) assess the impact of actionable information on clinicians’ acceptance of early warnings and on perceived care plan quality.

Results

We are obtaining clinical and administrative datasets from 3 leading health care systems’ enterprise data warehouses. We plan to start data analysis in 2020 and finish our study in 2025.

Conclusions

Techniques to be developed in this study can boost risk warning timeliness, model accuracy, and generalizability; improve patient finding for preventive care; help form tailored care plans; advance machine learning for many clinical applications; and be generalized for many other chronic diseases.

International Registered Report Identifier (IRRID)

PRR1-10.2196/13783

Experience and reflection from China's Xiangya medical big data project

The construction of medical big data includes several problems that need to be solved, such as integration and data sharing of many heterogeneous information systems, efficient processing and analysis of large-scale medical data with complex structure or low degree of structure, and narrow application range of medical data. Therefore, medical big data construction is not only a simple collection and application of medical data but also a complex systematic project. This paper introduces China's experience in the construction of a regional medical big data ecosystem, including the overall goal of the project; establishment of policies to encourage data sharing; handling the relationship between personal privacy, information security, and information availability; establishing a cooperation mechanism between agencies; designing a polycentric medical data acquisition system; and establishing a large data centre. From the experience gained from one of China's earliest established medical big data projects, we outline the challenges encountered during its development and recommend approaches to overcome these challenges to design medical big data projects in China more rationally. Clear and complete top-level design of a project requires to be planned in advance and considered carefully. It is essential to provide a culture of information sharing and to facilitate the opening of data, and changes in ideas and policies need the guidance of the government. The contradiction between data sharing and data security must be handled carefully, that is not to say data openness could be abandoned. The construction of medical big data involves many institutions, and high-level management and cooperation can significantly improve efficiency and promote innovation. Compared with infrastructure construction, it is more challenging and time-consuming to develop appropriate data standards, data integration tools and data mining tools.

Tailored Healthcare: Two Perspectives on the Development and Use of Patient Profiles

Calls for a more tailored approach to the management of cardiometabolic and musculoskeletal diseases have been increasing. Although tailored care is a centuries-old concept, it is still unclear how it should be best practised. The current paper introduces two phenotype-based Dutch approaches to support tailored care. One approach focuses on patients with type 2 diabetes, the other on patients undergoing total joint replacement. Using the patient profiling approach, both projects propose that care can be tailored by the assessment of biopsychosocial patient characteristics, stratification of patients into subgroups of patients with similar care needs, abilities, and preferences (so-called patient profiles) and tailoring of care in concordance with the common care preferences of these profiles. In this article, the advantages and disadvantages of the method are discussed to enable researchers or clinicians who want to extend the patient profiling approach to other patient populations to carefully evaluate these in relation to their project's focus and available resources.

FUNDING

Novo Nordisk B.V., the Netherlands Organisation for Scientific Research (NWO) (Grant 314-99-118) and Zimmer Biomet Inc.

Development and Feasibility of Bundled Payments for the Multidisciplinary Treatment of Head and Neck Cancer: A Pilot Program

Purpose:

Despite growing interest in bundled payments to reduce the costs of care, this payment method remains largely untested in cancer. This 3-year pilot tested the feasibility of a 1-year bundled payment for the multidisciplinary treatment of head and neck cancers.

Methods:

Four prospective treatment-based bundles were developed for patients with selected head and neck cancers. These risk-adjusted bundles covered 1 year of care that began with primary cancer treatment. Manual processes were developed for patient identification, enrollment, billing, and payment. Patients were prospectively identified and enrolled, and bundled payments were made at treatment start. Operational metrics tracked incremental effort for pilot processes and average payment cycle time compared with fee-for-service (FFS) payments.

Results:

This pilot confirmed the feasibility of a 1-year prospective bundled payment for head and neck cancers. Between November 2014 and October 2016, 88 patients were enrolled successfully with prospective bundled payments. Through September 2017, 94% of patients completed the pilot with 6% still enrolled. Manual pilot processes required more effort than anticipated; claims processing was the most time-consuming activity. The production of a bundle bill took an additional 15 minutes versus FFS billing. The average payment cycle time was 37 days (range, 15 to 141 days) compared with a 15-day average under FFS.

Conclusion:

Prospective bundled payments were successfully implemented in this pilot. Additional pilots should study this payment method in higher-volume cancers. Robust systems are needed to automate patient identification, enrollment, billing, and payment along with policies that reduce administrative burden and allow for the introduction of novel cancer therapies.

A Roadmap for Optimizing Asthma Care Management via Computational Approaches

Asthma affects 9% of Americans and incurs US $56 billion in cost, 439,000 hospitalizations, and 1.8 million emergency room visits annually. A small fraction of asthma patients with high vulnerabilities, severe disease, or great barriers to care consume most health care costs and resources. An effective approach is urgently needed to identify high-risk patients and intervene to improve outcomes and to reduce costs and resource use. Care management is widely used to implement tailored care plans for this purpose, but it is expensive and has limited service capacity. To maximize benefit, we should enroll only patients anticipated to have the highest costs or worst prognosis. Effective care management requires correctly identifying high-risk patients, but current patient identification approaches have major limitations. This paper pinpoints these limitations and outlines multiple machine learning techniques to address them, providing a roadmap for future research.

Business analytics-enabled decision-making effectiveness through knowledge absorptive capacity in health care

Purpose

Drawing on the resource-based theory and dynamic capability view, this paper aims to examine the mechanisms by which business analytics (BA) capabilities (i.e. the effective use of data aggregation, analytics and data interpretation tools) in healthcare units indirectly influence decision-making effectiveness through the mediating role of knowledge absorptive capacity.

Design/methodology/approach

Using a survey method, this study collected data from the hospitals in Taiwan. Of the 155 responses received, three were incomplete, giving a 35.84 per cent response rate with 152 valid data points. Structural equation modeling was used to test the hypotheses.

Findings

This study conceptualizes, operationalizes and measures the BA capability as a multi-dimensional construct that is formed by capturing the functionalities of BA systems in health care, leading to the conclusion that healthcare units are likely to obtain valuable knowledge through using the data analysis and interpretation tools effectively. The effective use of data analysis and interpretation tools in healthcare units indirectly influence decision-making effectiveness, an impact that is mediated by absorptive capacity.

Originality/value

This study adds values to the literature by conceptualizing BA capabilities in healthcare and demonstrating how knowledge absorption matters when implementing BA to the decision-making process. The mediating role of absorptive capacity not only provides a mechanism by which BA can contribute to decision-making practices but also offers a new solution to the puzzle of the IT productivity paradox in healthcare settings.

Informatics in neurocritical care: new ideas for Big Data

PURPOSE OF REVIEW

Big data is the new hype in business and healthcare. Data storage and processing has become cheap, fast, and easy. Business analysts and scientists are trying to design methods to mine these data for hidden knowledge. Neurocritical care is a field that typically produces large amounts of patient-related data, and these data are increasingly being digitized and stored. This review will try to look beyond the hype, and focus on possible applications in neurointensive care amenable to Big Data research that can potentially improve patient care.

RECENT FINDINGS

The first challenge in Big Data research will be the development of large, multicenter, and high-quality databases. These databases could be used to further investigate recent findings from mathematical models, developed in smaller datasets. Randomized clinical trials and Big Data research are complementary. Big Data research might be used to identify subgroups of patients that could benefit most from a certain intervention, or can be an alternative in areas where randomized clinical trials are not possible.

SUMMARY

The processing and the analysis of the large amount of patient-related information stored in clinical databases is beyond normal human cognitive ability. Big Data research applications have the potential to discover new medical knowledge, and improve care in the neurointensive care unit.

Leveraging a Statewide Clinical Data Warehouse to Expand Boundaries of the Learning Health System

Learning Health Systems (LHS) require accessible, usable health data and a culture of collaboration-a challenge for any single system, let alone disparate organizations, with macro- and micro-systems. Recently, the National Science Foundation described this important setting as a cyber-social ecosystem. In 2004, in an effort to create a platform for transforming health in South Carolina, Health Sciences South Carolina (HSSC) was established as a research collaboration of the largest health systems, academic medical centers and research intensive universities in South Carolina. With work beginning in 2010, HSSC unveiled an integrated Clinical Data Warehouse (CDW) in 2013 as a crucial anchor to a statewide LHS. This CDW integrates data from independent health systems in near-real time, and harmonizes the data for aggregation and use in research. With records from over 2.7 million unique patients spanning 9 years, this multi-institutional statewide clinical research repository allows integrated individualized patient-level data to be used for multiple population health and biomedical research purposes. In the first 21 months of operation, more than 2,800 de-identified queries occurred through i2b2, with 116 users. HSSC has developed and implemented solutions to complex issues emphasizing anti-competitiveness and participatory governance, and serves as a recognized model to organizations working to improve healthcare quality by extending the traditional borders of learning health systems.

Big data analytics to improve cardiovascular care: promise and challenges

The potential for big data analytics to improve cardiovascular quality of care and patient outcomes is tremendous. However, the application of big data in health care is at a nascent stage, and the evidence to date demonstrating that big data analytics will improve care and outcomes is scant. This Review provides an overview of the data sources and methods that comprise big data analytics, and describes eight areas of application of big data analytics to improve cardiovascular care, including predictive modelling for risk and resource use, population management, drug and medical device safety surveillance, disease and treatment heterogeneity, precision medicine and clinical decision support, quality of care and performance measurement, and public health and research applications. We also delineate the important challenges for big data applications in cardiovascular care, including the need for evidence of effectiveness and safety, the methodological issues such as data quality and validation, and the critical importance of clinical integration and proof of clinical utility. If big data analytics are shown to improve quality of care and patient outcomes, and can be successfully implemented in cardiovascular practice, big data will fulfil its potential as an important component of a learning health-care system.

Commentary: Epidemiology in the era of big data

Big Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called "three V's": variety, volume, and velocity. From this definition, we argue that Big Data has evolutionary and revolutionary implications for identifying and intervening on the determinants of population health. We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field's future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future.

Big data are coming to psychiatry: a general introduction

Big data are coming to the study of bipolar disorder and all of psychiatry. Data are coming from providers and payers (including EMR, imaging, insurance claims and pharmacy data), from omics (genomic, proteomic, and metabolomic data), and from patients and non-providers (data from smart phone and Internet activities, sensors and monitoring tools). Analysis of the big data will provide unprecedented opportunities for exploration, descriptive observation, hypothesis generation, and prediction, and the results of big data studies will be incorporated into clinical practice. Technical challenges remain in the quality, analysis and management of big data. This paper discusses some of the fundamental opportunities and challenges of big data for psychiatry.

Challenges Associated With Using Large Data Sets for Quality Assessment and Research in Clinical Settings

The rapidly expanding use of electronic records in health-care settings is generating unprecedented quantities of data available for clinical, epidemiological, and cost-effectiveness research. Several challenges are associated with using these data for clinical research, including issues surrounding access and information security, poor data quality, inconsistency of data within and across institutions, and a paucity of staff with expertise to manage and manipulate large clinical data sets. In this article, we describe our experience with assembling a data-mart and conducting clinical research using electronic data from four facilities within a single hospital network in New York City. We culled data from several electronic sources, including the institution's admission-discharge-transfer system, cost accounting system, electronic health record, clinical data warehouse, and departmental records. The final data-mart contained information for more than 760,000 discharges occurring from 2006 through 2012. Using categories identified by the National Institutes of Health Big Data to Knowledge initiative as a framework, we outlined challenges encountered during the development and use of a domain-specific data-mart and recommend approaches to overcome these challenges.