Guiding interoperable electronic health records through patient-sharing networks

A picture of health: determining the core population served by an urban NHS hospital trust and understanding the key health needs

Background

NHS hospitals do not have clearly defined geographic populations to whom they provide care, with patients able to attend any hospital. Identifying a core population for a hospital trust, particularly those in urban areas where there are multiple providers and high population churn, is essential to understanding local key health needs especially given the move to integrated care systems. This can enable effective planning and delivery of preventive interventions and community engagement, rather than simply treating those presenting to services. In this article we describe a practical method for identifying a hospital’s catchment population based on where potential patients are most likely to reside, and describe that population’s size, demographic and social profile, and the key health needs.

Methods

A 30% proportional flow method was used to identify a catchment population using an acute hospital trust in West London as an example. Records of all hospital attendances between 1^st April 2017 and 31^st March 2018 were analysed using Hospital Episode Statistics. Any Lower Layer Super Output Areas where 30% or more of residents who attended any hospital for care did so at the example trust were assigned to the catchment area. Publicly available local and national datasets were then applied to identify and describe the population’s key health needs.

Results

A catchment comprising 617,709 people, of an equal gender-split (50.4% male) and predominantly working age (15 to 64 years) population was identified. Thirty nine point six percent of residents identified as being from Black and Minority Ethnic (BAME) groups, a similar proportion that reported being born abroad, with over 85 languages spoken. Health indicators were estimated, including: a healthy life expectancy difference of over twenty years; bowel cancer screening coverage of 48.8%; chlamydia diagnosis rates of 2,136 per 100,000; prevalence of visible dental decay among five-year-olds of 27.9%.

Conclusions

We define a blueprint by which a catchment can be defined for a hospital trust and demonstrate the value a hospital-view of the local population could provide in understanding local health needs and enabling population-level health improvement interventions. While an individual approach allows tailoring to local context and need, there could be an efficiency saving were such public health information made routinely and regularly available for every NHS hospital.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12373-5.

The Pipeline for Standardizing Russian Unstructured Allergy Anamnesis Using FHIR AllergyIntolerance Resource

BACKGROUND

 The larger part of essential medical knowledge is stored as free text which is complicated to process. Standardization of medical narratives is an important task for data exchange, integration, and semantic interoperability.

OBJECTIVES

 The article aims to develop the end-to-end pipeline for structuring Russian free-text allergy anamnesis using international standards.

METHODS

 The pipeline for free-text data standardization is based on FHIR (Fast Healthcare Interoperability Resources) and SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) to ensure semantic interoperability. The pipeline solves common tasks such as data preprocessing, classification, categorization, entities extraction, and semantic codes assignment. Machine learning methods, rule-based, and dictionary-based approaches were used to compose the pipeline. The pipeline was evaluated on 166 randomly chosen medical records.

RESULTS

 AllergyIntolerance resource was used to represent allergy anamnesis. The module for data preprocessing included the dictionary with over 90,000 words, including specific medication terms, and more than 20 regular expressions for errors correction, classification, and categorization modules resulted in four dictionaries with allergy terms (total 2,675 terms), which were mapped to SNOMED CT concepts. F-scores for different steps are: 0.945 for filtering, 0.90 to 0.96 for allergy categorization, 0.90 and 0.93 for allergens reactions extraction, respectively. The allergy terminology coverage is more than 95%.

CONCLUSION

 The proposed pipeline is a step to ensure semantic interoperability of Russian free-text medical records and could be effective in standardization systems for further data exchange and integration.

Electronic Health Records, Interoperability and Patient Safety in Health Systems of High-income Countries: A Systematic Review Protocol

INTRODUCTION

The availability and routine use of electronic health records (EHRs) have become commonplace in healthcare systems of many high-income countries. While there is an ever-growing body of literature pertaining to their use, evidence surrounding the importance of EHR interoperability and its impact on patient safety remains less clear. There is, therefore, a need and opportunity to evaluate the evidence available regarding this relationship so as to better inform health informatics development and policies in the years to come. This systematic review aims to evaluate the impact of EHR interoperability on patient safety in health systems of high-income countries.

METHODS AND ANALYSIS

A systematic literature review will be conducted via a computerised search through four databases: PubMed, Embase, Health Management Information Consortium and PsycInfo for relevant articles published between 2010 and 2020. Outcomes of interest will include impact on patient safety and the broader effects on health systems. Quality of the randomised quantitative studies will be assessed using Cochrane Risk of Bias Tool. Non-randomised papers will be evaluated with the Risk of Bias In Non-Randomised Studies-of Interventions tool. Drummond's Checklist will be used for publications pertaining to economic evaluation. The National Institute for Health and Care Excellence quality appraisal checklist will be used to assess qualitative studies. A narrative synthesis will be conducted for included studies, and the body of evidence will be summarised in a summary of findings table.

ETHICS AND DISSEMINATION

This review will summarise published studies with non-identifiable data and, thus, does not require ethical approval. Findings will be disseminated through preprints, open access peer-reviewed publications, and conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42020209285.

Identifying naturally occurring communities of primary care providers in the English National Health Service in London

OBJECTIVES

Primary Care Networks (PCNs) are a new organisational hierarchy with wide-ranging responsibilities introduced in the National Health Service (NHS) Long Term Plan. The vision is that PCNs should represent 'natural' communities of general practices (GP practices) collaborating at scale and covering a geography that fits well with practices, other healthcare providers and local communities. Our study aims to identify natural communities of GP practices based on patient registration patterns using Markov Multiscale Community Detection, an unsupervised network-based clustering technique to create catchments for these communities.

DESIGN

Retrospective observational study using Hospital Episode Statistics - patient-level administrative records of attendances to hospital.

SETTING

General practices in the 32 Clinical Commissioning Groups of Greater London PARTICIPANTS: All adult patients resident in and registered to a GP practice in Greater London that had one or more outpatient encounters at NHS hospitals between 1^st April 2017 and 31^st March 2018.

MAIN OUTCOME MEASURES

The allocation of GP practices in Greater London to PCNs based on the registrations of patients resident in each Lower Layer Super Output Area (LSOA) of Greater London. The population size and coverage of each proposed PCN.

RESULTS

3 428 322 unique patients attended 1334 GPs in 4835 LSOAs in Greater London. Our model grouped 1291 GPs (96.8%) and 4721 LSOAs (97.6%) into 165 mutually exclusive PCNs. Median PCN list size was 53 490, with a lower quartile of 38 079 patients and an upper quartile of 72 982 patients. A median of 70.1% of patients attended a GP within their allocated PCN, ranging from 44.6% to 91.4%.

CONCLUSIONS

With PCNs expected to take a role in population health management and with community providers expected to reconfigure around them, it is vital to recognise how PCNs represent their communities. Our method may be used by policymakers to understand the populations and geography shared between networks.

Improving data sharing between acute hospitals in England: an overview of health record system distribution and retrospective observational analysis of inter-hospital transitions of care

OBJECTIVES

To determine the frequency of use and spatial distribution of health record systems in the English National Health Service (NHS). To quantify transitions of care between acute hospital trusts and health record systems to guide improvements to data sharing and interoperability.

DESIGN

Retrospective observational study using Hospital Episode Statistics.

SETTING

Acute hospital trusts in the NHS in England.

PARTICIPANTS

All adult patients resident in England that had one or more inpatient, outpatient or accident and emergency encounters at acute NHS hospital trusts between April 2017 and April 2018.

PRIMARY AND SECONDARY OUTCOME MEASURES

Frequency of use and spatial distribution of health record systems. Frequency and spatial distribution of transitions of care between hospital trusts and health record systems.

RESULTS

21 286 873 patients were involved in 121 351 837 encounters at 152 included trusts. 117 (77.0%) hospital trusts were using electronic health records (EHR). There was limited regional alignment of EHR systems. On 11 017 767 (9.1%) occasions, patients attended a hospital using a different health record system to their previous hospital attendance. 15 736 863 (73.9%) patients had two or more encounters with the included trusts and 3 931 255 (25.0%) of those attended two or more trusts. Over half (53.6%) of these patients had encounters shared between just 20 pairs of hospitals. Only two of these pairs of trusts used the same EHR system.

CONCLUSIONS

Each year, millions of patients in England attend two or more different hospital trusts. Most of the pairs of trusts that commonly share patients do not use the same record systems. This research highlights significant barriers to inter-hospital data sharing and interoperability. Findings from this study can be used to improve electronic health record system coordination and develop targeted approaches to improve interoperability. The methods used in this study could be used in other healthcare systems that face the same interoperability challenges.