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
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...
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BMJ Publishing Group
2024-02-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/14/2/e080678.full |
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| author | Tom Marshall Elizabeth Sapey Miles D Witham Steve Harris Rachel Cooper Chris Plummer Felicity Evison James Wason Heather J Cordell Suzy Gallier Fiona E Matthews Ewan Pearson Avan A Sayer Mervyn Singer Joanne Field Mohammed Osman Sian Robinson Victoria Bartle Thomas Scharf Jadene Lewis Rominique Doal Peta le Roux Ray Holding Paolo Missier |
| author_facet | Tom Marshall Elizabeth Sapey Miles D Witham Steve Harris Rachel Cooper Chris Plummer Felicity Evison James Wason Heather J Cordell Suzy Gallier Fiona E Matthews Ewan Pearson Avan A Sayer Mervyn Singer Joanne Field Mohammed Osman Sian Robinson Victoria Bartle Thomas Scharf Jadene Lewis Rominique Doal Peta le Roux Ray Holding Paolo Missier |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-703b51a843cb4454bc461a458024a6fe |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | BMJ Publishing Group |
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| series | BMJ Open |
| spelling | doaj-art-703b51a843cb4454bc461a458024a6fe2025-02-01T23:40:10ZengBMJ Publishing GroupBMJ Open2044-60552024-02-0114210.1136/bmjopen-2023-080678How far back do we need to look to capture diagnoses in electronic health records? A retrospective observational study of hospital electronic health record data 0Tom Marshall1Elizabeth Sapey2Miles D Witham3Steve Harris4Rachel Cooper5Chris Plummer6Felicity Evison7James Wason8Heather J CordellSuzy Gallier9Fiona E MatthewsEwan Pearson10Avan A Sayer11Mervyn Singer12Joanne Field13Mohammed Osman14Sian RobinsonVictoria BartleThomas ScharfJadene Lewis15Rominique Doal16Peta le Roux17Ray HoldingPaolo Missier11 Kenya National Bureau of Statistics, Nairobi, Nairobi, Kenya2 Institute of Applied Health Research, University of Birmingham, Birmingham, UK1 Institute of Inflammation and Ageing, University of Birmingham College of Medical and Dental Sciences, Birmingham, UKAGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UKCritical Care Department, University College London Hospitals NHS Foundation Trust, London, UKAGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UKTranslational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE1 7RU, UKData Science Team, Research Development and Innovation, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UKMRC Biostatistics Unit, University of Cambridge, UKPIONEER Health Data Research Hub, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UKUniversity of Dundee, Dundee, UKAGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UKCritical Care Department, University College London Hospitals NHS Foundation Trust, London, UKGenomics and Molecular Medicine Service, Nottingham University Hospitals NHS Trust, Nottingham, UKAGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UKPIONEER Hub, University of Birmingham, Birmingham, UKPIONEER Hub, University of Birmingham, Birmingham, UKDigital Services, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UKObjectives 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.https://bmjopen.bmj.com/content/14/2/e080678.full |
| spellingShingle | Tom Marshall Elizabeth Sapey Miles D Witham Steve Harris Rachel Cooper Chris Plummer Felicity Evison James Wason Heather J Cordell Suzy Gallier Fiona E Matthews Ewan Pearson Avan A Sayer Mervyn Singer Joanne Field Mohammed Osman Sian Robinson Victoria Bartle Thomas Scharf Jadene Lewis Rominique Doal Peta le Roux Ray Holding Paolo Missier 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 |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://bmjopen.bmj.com/content/14/2/e080678.full |
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