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Using an Ontology to Facilitate More Accurate Coding of Social Prescriptions Addressing Social Determinants of Health: Feasibility Study

BACKGROUND: National Health Service (NHS) England supports social prescribing in order to address social determinants of health, which account for approximately 80% of all health outcomes. Nevertheless, data on ongoing social prescribing activities are lacking. Although NHS England has attempted to...

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Autores principales: Jani, Anant, Liyanage, Harshana, Okusi, Cecilia, Sherlock, Julian, Hoang, Uy, Ferreira, Filipa, Yonova, Ivelina, de Lusignan, Simon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762682/
https://www.ncbi.nlm.nih.gov/pubmed/33306032
http://dx.doi.org/10.2196/23721
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author Jani, Anant
Liyanage, Harshana
Okusi, Cecilia
Sherlock, Julian
Hoang, Uy
Ferreira, Filipa
Yonova, Ivelina
de Lusignan, Simon
author_facet Jani, Anant
Liyanage, Harshana
Okusi, Cecilia
Sherlock, Julian
Hoang, Uy
Ferreira, Filipa
Yonova, Ivelina
de Lusignan, Simon
author_sort Jani, Anant
collection PubMed
description BACKGROUND: National Health Service (NHS) England supports social prescribing in order to address social determinants of health, which account for approximately 80% of all health outcomes. Nevertheless, data on ongoing social prescribing activities are lacking. Although NHS England has attempted to overcome this problem by recommending 3 standardized primary care codes, these codes do not capture the social prescribing activity to a level of granularity that would allow for fair attribution of outcomes to social prescribing. OBJECTIVE: In this study, we explored whether an alternative approach to coding social prescribing activity, specifically through a social prescribing ontology, can be used to capture the social prescriptions used in primary care in greater detail. METHODS: The social prescribing ontology, implemented according to the Web Ontology Language, was designed to cover several key concepts encompassing social determinants of health. Readv2 and Clinical Terms Version 3 codes were identified using the NHS Terms Browser. The Royal College of General Practitioners Research Surveillance Centre, a sentinel network of over 1000 primary care practices across England covering a population of more than 4,000,000 registered patients, was used for data analyses for a defined period (ie, January 2011 to December 2019). RESULTS: In all, 668 codes capturing social prescriptions addressing different social determinants of health were identified for the social prescribing ontology. For the study period, social prescribing ontology codes were used 5,504,037 times by primary care practices of the Royal College of General Practitioners Research Surveillance Centre as compared to 29,606 instances of use of social prescribing codes, including NHS England’s recommended codes. CONCLUSIONS: A social prescribing ontology provides a powerful alternative to the codes currently recommended by NHS England to capture detailed social prescribing activity in England. The more detailed information thus obtained will allow for explorations about whether outputs or outcomes of care delivery can be attributed to social prescriptions, which is essential for demonstrating the overall value that social prescribing can deliver to the NHS and health care systems.
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spelling pubmed-77626822020-12-29 Using an Ontology to Facilitate More Accurate Coding of Social Prescriptions Addressing Social Determinants of Health: Feasibility Study Jani, Anant Liyanage, Harshana Okusi, Cecilia Sherlock, Julian Hoang, Uy Ferreira, Filipa Yonova, Ivelina de Lusignan, Simon J Med Internet Res Original Paper BACKGROUND: National Health Service (NHS) England supports social prescribing in order to address social determinants of health, which account for approximately 80% of all health outcomes. Nevertheless, data on ongoing social prescribing activities are lacking. Although NHS England has attempted to overcome this problem by recommending 3 standardized primary care codes, these codes do not capture the social prescribing activity to a level of granularity that would allow for fair attribution of outcomes to social prescribing. OBJECTIVE: In this study, we explored whether an alternative approach to coding social prescribing activity, specifically through a social prescribing ontology, can be used to capture the social prescriptions used in primary care in greater detail. METHODS: The social prescribing ontology, implemented according to the Web Ontology Language, was designed to cover several key concepts encompassing social determinants of health. Readv2 and Clinical Terms Version 3 codes were identified using the NHS Terms Browser. The Royal College of General Practitioners Research Surveillance Centre, a sentinel network of over 1000 primary care practices across England covering a population of more than 4,000,000 registered patients, was used for data analyses for a defined period (ie, January 2011 to December 2019). RESULTS: In all, 668 codes capturing social prescriptions addressing different social determinants of health were identified for the social prescribing ontology. For the study period, social prescribing ontology codes were used 5,504,037 times by primary care practices of the Royal College of General Practitioners Research Surveillance Centre as compared to 29,606 instances of use of social prescribing codes, including NHS England’s recommended codes. CONCLUSIONS: A social prescribing ontology provides a powerful alternative to the codes currently recommended by NHS England to capture detailed social prescribing activity in England. The more detailed information thus obtained will allow for explorations about whether outputs or outcomes of care delivery can be attributed to social prescriptions, which is essential for demonstrating the overall value that social prescribing can deliver to the NHS and health care systems. JMIR Publications 2020-12-11 /pmc/articles/PMC7762682/ /pubmed/33306032 http://dx.doi.org/10.2196/23721 Text en ©Anant Jani, Harshana Liyanage, Cecilia Okusi, Julian Sherlock, Uy Hoang, Filipa Ferreira, Ivelina Yonova, Simon de Lusignan. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 11.12.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Jani, Anant
Liyanage, Harshana
Okusi, Cecilia
Sherlock, Julian
Hoang, Uy
Ferreira, Filipa
Yonova, Ivelina
de Lusignan, Simon
Using an Ontology to Facilitate More Accurate Coding of Social Prescriptions Addressing Social Determinants of Health: Feasibility Study
title Using an Ontology to Facilitate More Accurate Coding of Social Prescriptions Addressing Social Determinants of Health: Feasibility Study
title_full Using an Ontology to Facilitate More Accurate Coding of Social Prescriptions Addressing Social Determinants of Health: Feasibility Study
title_fullStr Using an Ontology to Facilitate More Accurate Coding of Social Prescriptions Addressing Social Determinants of Health: Feasibility Study
title_full_unstemmed Using an Ontology to Facilitate More Accurate Coding of Social Prescriptions Addressing Social Determinants of Health: Feasibility Study
title_short Using an Ontology to Facilitate More Accurate Coding of Social Prescriptions Addressing Social Determinants of Health: Feasibility Study
title_sort using an ontology to facilitate more accurate coding of social prescriptions addressing social determinants of health: feasibility study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762682/
https://www.ncbi.nlm.nih.gov/pubmed/33306032
http://dx.doi.org/10.2196/23721
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