How far back do we need to look to capture diagnoses in electronic health records? A retrospective observational study of hospital electronic health record data

The ADMISSION Research Collaborative; Jadene Lewis; Felicity Evison; Rominique Doal; Joanne Field; Suzy Gallier; Steve Harris; Peta le Roux; Mohammed Osman; Chris Plummer; Elizabeth Sapey; Mervyn Singer; Avan A Sayer; Miles D  Witham

doi:10.1136/bmjopen-2023-080678

How far back do we need to look to capture diagnoses in electronic health records? A retrospective observational study of hospital electronic health record data

The ADMISSION Research Collaborative, Jadene Lewis, Felicity Evison, Rominique Doal, Joanne Field, Suzy Gallier, Steve Harris, Peta le Roux, Mohammed Osman, Chris Plummer, Elizabeth Sapey, Mervyn Singer, Avan A Sayer, Miles D Witham^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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Abstract

Objectives: Analysis of routinely collected electronic health data is a key tool for long-term condition research and practice for hospitalised patients. This requires accurate and complete ascertainment of a broad range of diagnoses, something not always recorded on an admission document at a single point in time. This study aimed to ascertain how far back in time electronic hospital records need to be interrogated to capture long-term condition diagnoses.

Design: Retrospective observational study of routinely collected hospital electronic health record data.

Setting: Queen Elizabeth Hospital Birmingham (UK)-linked data held by the PIONEER acute care data hub.

Participants: Patients whose first recorded admission for chronic obstructive pulmonary disease (COPD) exacerbation (n=560) or acute stroke (n=2142) was between January and December 2018 and who had a minimum of 10 years of data prior to the index date.

Outcome measures: We identified the most common International Classification of Diseases version 10-coded diagnoses received by patients with COPD and acute stroke separately. For each diagnosis, we derived the number of patients with the diagnosis recorded at least once over the full 10-year lookback period, and then compared this with shorter lookback periods from 1 year to 9 years prior to the index admission.

Results: Seven of the top 10 most common diagnoses in the COPD dataset reached >90% completeness by 6 years of lookback. Atrial fibrillation and diabetes were >90% coded with 2–3 years of lookback, but hypertension and asthma completeness continued to rise all the way out to 10 years of lookback. For stroke, 4 of the top 10 reached 90% completeness by 5 years of lookback; angina pectoris was >90% coded at 7 years and previous transient ischaemic attack completeness continued to rise out to 10 years of lookback.

Conclusion: A 7-year lookback captures most, but not all, common diagnoses. Lookback duration should be tailored to the conditions being studied.

Original language	English
Article number	e080678
Number of pages	7
Journal	BMJ open
Volume	14
Issue number	2
DOIs	https://doi.org/10.1136/bmjopen-2023-080678
Publication status	Published - 13 Feb 2024

Bibliographical note

Funding:
This research was conducted as part of the ADMISSION Research Collaborative, funded by the Strategic Priority Fund ‘Tackling multimorbidity at scale’ Programme (grant number MR/V033654/1). This funding is delivered by the Medical Research Council and the National Institute for Health and Care Research in partnership with the Economic and Social Research Council and in collaboration with the Engineering and Physical Sciences Research Council.

Keywords

Electronic Health Records
Hospitals
Information Extraction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1136/bmjopen-2023-080678Licence: Creative Commons: Attribution (CC BY)

LewisJ2024HowfarFinal published version, 506 KBLicence: Creative Commons: Attribution (CC BY)

ADMISSION UK Multimorbidity Research Collaborative on Multiple Long-Term Conditions in Hospital: from burden and inequalities to underlying mechanisms
Sapey, E. & Marshall, T.
Medical Research Council
1/03/21 → 28/02/25
Project: Research Councils

Cite this

The ADMISSION Research Collaborative, Lewis, J., Evison, F., Doal, R., Field, J., Gallier, S., Harris, S., le Roux, P., Osman, M., Plummer, C., Sapey, E., Singer, M., Sayer, A. A., & Witham, M. D. (2024). How far back do we need to look to capture diagnoses in electronic health records? A retrospective observational study of hospital electronic health record data. BMJ open, 14(2), Article e080678. https://doi.org/10.1136/bmjopen-2023-080678

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abstract = "Objectives: Analysis of routinely collected electronic health data is a key tool for long-term condition research and practice for hospitalised patients. This requires accurate and complete ascertainment of a broad range of diagnoses, something not always recorded on an admission document at a single point in time. This study aimed to ascertain how far back in time electronic hospital records need to be interrogated to capture long-term condition diagnoses. Design: Retrospective observational study of routinely collected hospital electronic health record data. Setting: Queen Elizabeth Hospital Birmingham (UK)-linked data held by the PIONEER acute care data hub. Participants: Patients whose first recorded admission for chronic obstructive pulmonary disease (COPD) exacerbation (n=560) or acute stroke (n=2142) was between January and December 2018 and who had a minimum of 10 years of data prior to the index date. Outcome measures: We identified the most common International Classification of Diseases version 10-coded diagnoses received by patients with COPD and acute stroke separately. For each diagnosis, we derived the number of patients with the diagnosis recorded at least once over the full 10-year lookback period, and then compared this with shorter lookback periods from 1 year to 9 years prior to the index admission. Results: Seven of the top 10 most common diagnoses in the COPD dataset reached >90% completeness by 6 years of lookback. Atrial fibrillation and diabetes were >90% coded with 2–3 years of lookback, but hypertension and asthma completeness continued to rise all the way out to 10 years of lookback. For stroke, 4 of the top 10 reached 90% completeness by 5 years of lookback; angina pectoris was >90% coded at 7 years and previous transient ischaemic attack completeness continued to rise out to 10 years of lookback. Conclusion: A 7-year lookback captures most, but not all, common diagnoses. Lookback duration should be tailored to the conditions being studied.",

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author = "{The ADMISSION Research Collaborative} and Jadene Lewis and Felicity Evison and Rominique Doal and Joanne Field and Suzy Gallier and Steve Harris and {le Roux}, Peta and Mohammed Osman and Chris Plummer and Elizabeth Sapey and Mervyn Singer and Sayer, {Avan A} and Witham, {Miles D} and Paolo Missier",

note = "Funding: This research was conducted as part of the ADMISSION Research Collaborative, funded by the Strategic Priority Fund {\textquoteleft}Tackling multimorbidity at scale{\textquoteright} Programme (grant number MR/V033654/1). This funding is delivered by the Medical Research Council and the National Institute for Health and Care Research in partnership with the Economic and Social Research Council and in collaboration with the Engineering and Physical Sciences Research Council.",

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The ADMISSION Research Collaborative, Lewis, J, Evison, F, Doal, R, Field, J, Gallier, S, Harris, S, le Roux, P, Osman, M, Plummer, C, Sapey, E, Singer, M, Sayer, AA & Witham, MD 2024, 'How far back do we need to look to capture diagnoses in electronic health records? A retrospective observational study of hospital electronic health record data', BMJ open, vol. 14, no. 2, e080678. https://doi.org/10.1136/bmjopen-2023-080678

TY - JOUR

T1 - How far back do we need to look to capture diagnoses in electronic health records? A retrospective observational study of hospital electronic health record data

AU - The ADMISSION Research Collaborative

AU - Lewis, Jadene

AU - Evison, Felicity

AU - Doal, Rominique

AU - Field, Joanne

AU - Gallier, Suzy

AU - Harris, Steve

AU - le Roux, Peta

AU - Osman, Mohammed

AU - Plummer, Chris

AU - Sapey, Elizabeth

AU - Singer, Mervyn

AU - Sayer, Avan A

AU - Witham, Miles D

AU - Missier, Paolo

N1 - Funding: This research was conducted as part of the ADMISSION Research Collaborative, funded by the Strategic Priority Fund ‘Tackling multimorbidity at scale’ Programme (grant number MR/V033654/1). This funding is delivered by the Medical Research Council and the National Institute for Health and Care Research in partnership with the Economic and Social Research Council and in collaboration with the Engineering and Physical Sciences Research Council.

PY - 2024/2/13

Y1 - 2024/2/13

N2 - Objectives: Analysis of routinely collected electronic health data is a key tool for long-term condition research and practice for hospitalised patients. This requires accurate and complete ascertainment of a broad range of diagnoses, something not always recorded on an admission document at a single point in time. This study aimed to ascertain how far back in time electronic hospital records need to be interrogated to capture long-term condition diagnoses. Design: Retrospective observational study of routinely collected hospital electronic health record data. Setting: Queen Elizabeth Hospital Birmingham (UK)-linked data held by the PIONEER acute care data hub. Participants: Patients whose first recorded admission for chronic obstructive pulmonary disease (COPD) exacerbation (n=560) or acute stroke (n=2142) was between January and December 2018 and who had a minimum of 10 years of data prior to the index date. Outcome measures: We identified the most common International Classification of Diseases version 10-coded diagnoses received by patients with COPD and acute stroke separately. For each diagnosis, we derived the number of patients with the diagnosis recorded at least once over the full 10-year lookback period, and then compared this with shorter lookback periods from 1 year to 9 years prior to the index admission. Results: Seven of the top 10 most common diagnoses in the COPD dataset reached >90% completeness by 6 years of lookback. Atrial fibrillation and diabetes were >90% coded with 2–3 years of lookback, but hypertension and asthma completeness continued to rise all the way out to 10 years of lookback. For stroke, 4 of the top 10 reached 90% completeness by 5 years of lookback; angina pectoris was >90% coded at 7 years and previous transient ischaemic attack completeness continued to rise out to 10 years of lookback. Conclusion: A 7-year lookback captures most, but not all, common diagnoses. Lookback duration should be tailored to the conditions being studied.

AB - Objectives: Analysis of routinely collected electronic health data is a key tool for long-term condition research and practice for hospitalised patients. This requires accurate and complete ascertainment of a broad range of diagnoses, something not always recorded on an admission document at a single point in time. This study aimed to ascertain how far back in time electronic hospital records need to be interrogated to capture long-term condition diagnoses. Design: Retrospective observational study of routinely collected hospital electronic health record data. Setting: Queen Elizabeth Hospital Birmingham (UK)-linked data held by the PIONEER acute care data hub. Participants: Patients whose first recorded admission for chronic obstructive pulmonary disease (COPD) exacerbation (n=560) or acute stroke (n=2142) was between January and December 2018 and who had a minimum of 10 years of data prior to the index date. Outcome measures: We identified the most common International Classification of Diseases version 10-coded diagnoses received by patients with COPD and acute stroke separately. For each diagnosis, we derived the number of patients with the diagnosis recorded at least once over the full 10-year lookback period, and then compared this with shorter lookback periods from 1 year to 9 years prior to the index admission. Results: Seven of the top 10 most common diagnoses in the COPD dataset reached >90% completeness by 6 years of lookback. Atrial fibrillation and diabetes were >90% coded with 2–3 years of lookback, but hypertension and asthma completeness continued to rise all the way out to 10 years of lookback. For stroke, 4 of the top 10 reached 90% completeness by 5 years of lookback; angina pectoris was >90% coded at 7 years and previous transient ischaemic attack completeness continued to rise out to 10 years of lookback. Conclusion: A 7-year lookback captures most, but not all, common diagnoses. Lookback duration should be tailored to the conditions being studied.

KW - Electronic Health Records

KW - Hospitals

KW - Information Extraction

U2 - 10.1136/bmjopen-2023-080678

DO - 10.1136/bmjopen-2023-080678

M3 - Article

SN - 2044-6055

VL - 14

JO - BMJ open

JF - BMJ open

IS - 2

M1 - e080678

ER -

How far back do we need to look to capture diagnoses in electronic health records? A retrospective observational study of hospital electronic health record data

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

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Projects

ADMISSION UK Multimorbidity Research Collaborative on Multiple Long-Term Conditions in Hospital: from burden and inequalities to underlying mechanisms

Cite this