The European Institute for Innovation through Health Data

Generalizability of a Musculoskeletal Therapist Electronic Health Record for Modelling Outcomes to Work-Related Musculoskeletal Disorders

PURPOSE

Electronic Health Records (EHRs) can contain vast amounts of clinical information that could be reused in modelling outcomes of work-related musculoskeletal disorders (WMSDs). Determining the generalizability of an EHR dataset is an important step in determining the appropriateness of its reuse. The study aims to describe the EHR dataset used by occupational musculoskeletal therapists and determine whether the EHR dataset is generalizable to the Australian workers' population and injury characteristics seen in workers' compensation claims.

METHODS

Variables were considered if they were associated with outcomes of WMSDs and variables data were available. Completeness and external validity assessment analysed frequency distributions, percentage of records and confidence intervals.

RESULTS

There were 48,434 patient care plans across 10 industries from 2014 to 2021. The EHR collects information related to clinical interventions, health and psychosocial factors, job demands, work accommodations as well as workplace culture, which have all been shown to be valuable variables in determining outcomes to WMSDs. Distributions of age, duration of employment, gender and region of birth were mostly similar to the Australian workforce. Upper limb WMSDs were higher in the EHR compared to workers' compensation claims and diagnoses were similar.

CONCLUSION

The study shows the EHR has strong potential to be used for further research into WMSDs as it has a similar population to the Australian workforce, manufacturing industry and workers' compensation claims. It contains many variables that may be relevant in modelling outcomes to WMSDs that are not typically available in existing datasets.

Attaining consensus on a core dataset for upper limb lymphoedema using the Delphi method: A foundational step in creating a clinical support system

Background: Lymphoedema is a condition of localised swelling caused by a compromised lymphatic system. The protein-rich fluid accumulating in the interstitial tissue can create inflammation and irreversible changes to the skin and underlying tissue. An array of methods has been used to assess and report these changes. Heterogeneity is evident in the clinic and in the literature for the domains assessed, outcomes and outcome measures selected, measurement protocols followed, methods of analysis, and descriptors used to report change. Objective: This study seeks consensus on the required items for inclusion in a core data set for upper limb lymphoedema to digitise the monitoring and reporting of upper limb lymphoedema. Methods: The breadth of outcomes and descriptors in common use were captured in prior studies by this research group. This list was refined by frequency and proposed to experts in the field (n = 70) through a two-round online modified Delphi study. These participants rated the importance of each item for inclusion in the dataset and identified outcomes or descriptors they felt were missing in Round 1. In Round 2, participants rated any new outcomes or descriptors proposed and preference for how numeric data is displayed. Results: The core dataset was confirmed on completion of Round 2. Interlimb difference as a percentage, and limb volume were preferred for graphed display over time; and descriptors for observed and palpated change narrowed from 42 to 20. Conclusion: This dataset provides the foundation to create a clinical support system for upper limb lymphoedema.

Success and failure factors of patient recruitment for industry-sponsored clinical trials and the role of the electronic health records-a qualitative interview study in the Nordic countries

Background

Patient recruitment for clinical trials is challenging—only approximately one third of all trials recruit their participants as planned. The pharmaceutical industry’s views on recruitment success have not been comprehensively investigated, although the industry globally conducts almost one third of all clinical drug trials. This study explored patient recruitment success and failure factors and the role of electronic health records (EHR) in the recruitment of trial participants in the Nordic countries.

Methods

A descriptive qualitative interview study was conducted with 21 representatives of the pharmaceutical industry or contract research organizations operating in Finland, Sweden, Denmark, and Norway. The interviews covered 34 clinical pre-market drug trials. Qualitative data were analyzed using inductive content analysis.

Results

Four main categories were derived to represent both success and failure factors, whereas a fifth category represented only failure factors: (1) sponsor-related (protocol and trial preparation and feasibility evaluations), (2) site/investigator-related (access to patients, motivation, commitment and resources), (3) patient-related recruitment factors (medical need, patients’ role in their care and attitudes towards trials), (4) Sponsor—sites—patients collaboration factors, and (5) start-up related factors. EHR was the most important source of recruitment, utilized in 29 out of 34 trials discussed. Revision of the legislation regulating the secondary use of EHR was highlighted as the most effective measure to facilitate the use of EHR in recruitment of trial participants.

Conclusions

The industry representatives recognized quite well their own role in contributing to the success or failure of the recruitment: to facilitate recruitment of trial participants, many obstacles can be avoided with better trial preparation and proper feasibility evaluations. As access to patients represents one of the key success or failure factors of recruitment, and as the EHR is regarded the main source of searching for and finding patients, the development of EHR utilization appears to represent a powerful tool to improve patient recruitment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-022-06144-9.

Making Diabetes Electronic Medical Record Data Actionable: Promoting Benchmarking and Population Health Improvement Using the T1D Exchange Quality Improvement Portal

This article describes how the T1D Exchange Quality Improvement Collaborative leverages an innovative web platform, the QI Portal, to gather and store electronic medical record (EMR) data to promote benchmarking and population health improvement in a type 1 diabetes learning health system. The authors explain the value of the QI Portal, the process for mapping center-level data from EMRs using standardized data specifications, and the QI Portal's unique features for advancing population health.

Quality of Hospital Electronic Health Record (EHR) Data Based on the International Consortium for Health Outcomes Measurement (ICHOM) in Heart Failure: Pilot Data Quality Assessment Study

Background

There is increasing recognition that health care providers need to focus attention, and be judged against, the impact they have on the health outcomes experienced by patients. The measurement of health outcomes as a routine part of clinical documentation is probably the only scalable way of collecting outcomes evidence, since secondary data collection is expensive and error-prone. However, there is uncertainty about whether routinely collected clinical data within electronic health record (EHR) systems includes the data most relevant to measuring and comparing outcomes and if those items are collected to a good enough data quality to be relied upon for outcomes assessment, since several studies have pointed out significant issues regarding EHR data availability and quality.

Objective

In this paper, we first describe a practical approach to data quality assessment of health outcomes, based on a literature review of existing frameworks for quality assessment of health data and multistakeholder consultation. Adopting this approach, we performed a pilot study on a subset of 21 International Consortium for Health Outcomes Measurement (ICHOM) outcomes data items from patients with congestive heart failure.

Methods

All available registries compatible with the diagnosis of heart failure within an EHR data repository of a general hospital (142,345 visits and 12,503 patients) were extracted and mapped to the ICHOM format. We focused our pilot assessment on 5 commonly used data quality dimensions: completeness, correctness, consistency, uniqueness, and temporal stability.

Results

We found high scores (>95%) for the consistency, completeness, and uniqueness dimensions. Temporal stability analyses showed some changes over time in the reported use of medication to treat heart failure, as well as in the recording of past medical conditions. Finally, the investigation of data correctness suggested several issues concerning the characterization of missing data values. Many of these issues appear to be introduced while mapping the IMASIS-2 relational database contents to the ICHOM format, as the latter requires a level of detail that is not explicitly available in the coded data of an EHR.

Conclusions

Overall, results of this pilot study revealed good data quality for the subset of heart failure outcomes collected at the Hospital del Mar. Nevertheless, some important data errors were identified that were caused by fundamentally different data collection practices in routine clinical care versus research, for which the ICHOM standard set was originally developed. To truly examine to what extent hospitals today are able to routinely collect the evidence of their success in achieving good health outcomes, future research would benefit from performing more extensive data quality assessments, including all data items from the ICHOM standards set and across multiple hospitals.

The European medical information framework: A novel ecosystem for sharing healthcare data across Europe

INTRODUCTION

The European medical information framework (EMIF) was an Innovative Medicines Initiative project jointly supported by the European Union and the European Federation of Pharmaceutical Industries and Associations, that generated a common technology and governance framework to identify, assess and (re)use healthcare data, to facilitate real-world data research. The objectives of EMIF included providing a unified platform to support a wide range of studies within two verification programmes-Alzheimer's disease (EMIF-AD), and metabolic consequences of obesity (EMIF-MET).

METHODS

The EMIF platform was built around two main data-types: electronic health record data and research cohort data, and the platform architecture composed of a set of tools designed to enable data discovery and characterisation. This included the EMIF catalogue, which allowed users to find relevant data sources, including the data-types collected. Data harmonisation via a common data model were central to the project especially for population data sources. EMIF also developed an ethical code of practice to ensure data protection, patient confidentiality and compliance with the European Data Protection Directive, and GDPR.

RESULTS

Currently 18 population-based disease agnostic and 60 cohort-based Alzheimer's data partners from across 14 countries are contained within the catalogue, and this will continue to expand. The work conducted in EMIF-AD and EMIF-MET includes standardizing cohorts, summarising baseline characteristics of patients, developing diagnostic algorithms, epidemiological studies, identifying and validating novel biomarkers and selecting potential patient samples for pharmacological intervention.

CONCLUSIONS

EMIF was designed to provide a sustainable model as demonstrated by the sustainability plans for EMIF-AD. Although network-wide studies using EMIF were not conducted during this project to evaluate its sustainability, learning from EMIF will be used in the follow-on IMI-2 project, European Health Data and Evidence Network (EHDEN). Furthermore, EMIF has facilitated collaborations between partners and continues to promote a wider adoption of principles, technology and architecture through some of its continued work.

The French Health Data Hub and the German Medical Informatics Initiatives: Two National Projects to Promote Data Sharing in Healthcare

OBJECTIVE

The diversity and volume of health data have been rapidly increasing in recent years. While such big data hold significant promise for accelerating discovery, data use entails many challenges including the need for adequate computational infrastructure and secure processes for data sharing and access. In Europe, two nationwide projects have been launched recently to support these objectives. This paper compares the French Health Data Hub initiative (HDH) to the German Medical Informatics Initiatives (MII).

METHOD

We analysed the projects according to the following criteria: (i) Global approach and ambitions, (ii) Use cases, (iii) Governance and organization, (iv) Technical aspects and interoperability, and (v) Data privacy access/data governance.

RESULTS

The French and German projects share the same objectives but are different in terms of methodologies. The HDH project is based on a top-down approach and focuses on a shared computational infrastructure, providing tools and services to speed projects between data producers and data users. The MII project is based on a bottom-up approach and relies on four consortia including academic hospitals, universities, and private partners.

CONCLUSION

Both projects could benefit from each other. A Franco-German cooperation, extended to other countries of the European Union with similar initiatives, should allow sharing and strengthening efforts in a strategic area where competition from other countries has increased.

A Ledger of Me: Personalizing Healthcare Using Blockchain Technology

Personal Health Records (PHRs) have the potential to give patients fine-grained, personalized and secure access to their own medical data and to enable self-management of care. Emergent trends around the use of Blockchain, or Distributed Ledger Technology, seem to offer solutions to some of the problems faced in enabling these technologies, especially to support issues consent, data exchange, and data access. We present an analysis of existing blockchain-based health record solutions and a reference architecture for a "Ledger of Me" system that extends PHR to create a new platform combining the collection and access of medical data and digital interventions with smart contracts. Our intention is to enable patient use of the data in order to support their care and to provide a strong consent mechanisms for sharing of data between different organizations and apps. Ledger of Me is based on around the principle that this combination of event-driven smart contracts, medical record data, and patient control is important for the adoption of blockchain-based solutions for the PHR. The reference architecture we present can serve as the basis of a range of future blockchain-based medical application architectures.

Guiding interoperable electronic health records through patient-sharing networks

Effective sharing of clinical information between care providers is a critical component of a safe, efficient health system. National data-sharing systems may be costly, politically contentious and do not reflect local patterns of care delivery. This study examines hospital attendances in England from 2013 to 2015 to identify instances of patient sharing between hospitals. Of 19.6 million patients receiving care from 155 hospital care providers, 130 million presentations were identified. On 14.7 million occasions (12%), patients attended a different hospital to the one they attended on their previous interaction. A network of hospitals was constructed based on the frequency of patient sharing between hospitals which was partitioned using the Louvain algorithm into ten distinct data-sharing communities, improving the continuity of data sharing in such instances from 0 to 65-95%. Locally implemented data-sharing communities of hospitals may achieve effective accessibility of clinical information without a large-scale national interoperable information system.

Swiss Learning Health System: A national initiative to establish learning cycles for continuous health system improvement

The health system in Switzerland is considered as one of the best in the world. Nevertheless, to effectively and efficiently meet current and future challenges, an infrastructure and culture are needed where the best evidence is systematically made available and used, and the system evolves on the basis of a constant exchange between research, policy, and practice. The Swiss Learning Health System institutionalizes this idea as a multistakeholder national initiative to ensure continuous improvement through ongoing research and implementation. This article presents the objectives and mechanisms of the Swiss Learning Health System in the context of international initiatives to strengthen health systems and improve population health through learning cycles.

Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse

BACKGROUND

Enormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems.

OBJECTIVE

In this paper, we discuss whether data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of data.

METHODS

This paper is based on the authors' experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records.

RESULTS

We first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature.

CONCLUSIONS

There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven health care will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.