PMID- 31357992 OWN - NLM STAT- MEDLINE DCOM- 20191125 LR - 20231013 IS - 1472-6963 (Electronic) IS - 1472-6963 (Linking) VI - 19 IP - 1 DP - 2019 Jul 29 TI - Linking observational data from general practice, hospital admissions and diabetes clinic databases: can it be used to predict hospital admission? PG - 526 LID - 10.1186/s12913-019-4337-1 [doi] LID - 526 AB - BACKGROUND: Linking process of care data from general practice (GP) and hospital data may provide more information about the risk of hospital admission and re-admission for people with type-2 diabetes mellitus (T2DM). This study aimed to extract and link data from a hospital, a diabetes clinic (DC). A second aim was to determine whether the data could be used to predict hospital admission for people with T2DM. METHODS: Data were extracted using the GRHANITE extraction and linkage tool. The data from nine GPs and the DC included data from the two years prior to the hospital admission. The date of the first hospital admission for patients with one or more admissions was the index admission. For those patients without an admission, the census date 31/03/2014 was used in all outputs requiring results prior to an admission. Readmission was any admission following the index admission. The data were summarised to provide a comparison between two groups of patients: 1) Patients with a diagnosis of T2DM who had been treated at a GP and had a hospital admission and 2) Patients with a diagnosis of T2DM who had been treated at a GP and did not have a hospital admission. RESULTS: Data were extracted for 161,575 patients from the three data sources, 644 patients with T2DM had data linked between the GPs and the hospital. Of these, 170 also had data linked with the DC. Combining the data from the different data sources improved the overall data quality for some attributes particularly those attributes that were recorded consistently in the hospital admission data. The results from the modelling to predict hospital admission were plausible given the issues with data completeness. CONCLUSION: This project has established the methodology (tools and processes) to extract, link, aggregate and analyse data from general practices, hospital admission data and DC data. This study methodology involved the establishment of a comparator/control group from the same sites to compare and contrast the predictors of admission, addressing a limitation of most published risk stratification and admission prediction studies. Data completeness needs to be improved for this to be useful to predict hospital admissions. FAU - Dennis, Sarah AU - Dennis S AUID- ORCID: 0000-0003-2685-9246 AD - Faculty of Health Sciences, University of Sydney, 75 East Street, Lidcombe, NSW, 2141, Australia. AD - Centre for Primary Health Care and Equity, University of New South Wales Australia, Sydney, NSW, 2052, Australia. AD - Ingham Institute for Applied Medical Research, 1 Campbell Street, Liverpool, NSW, 2170, Australia. AD - South Western Sydney Local Health District, Liverpool, Liverpool, NSW, 2170, Australia. FAU - Taggart, Jane AU - Taggart J AD - Centre for Primary Health Care and Equity, University of New South Wales Australia, Sydney, NSW, 2052, Australia. FAU - Yu, Hairong AU - Yu H AD - Centre for Primary Health Care and Equity, University of New South Wales Australia, Sydney, NSW, 2052, Australia. FAU - Jalaludin, Bin AU - Jalaludin B AD - Ingham Institute for Applied Medical Research, 1 Campbell Street, Liverpool, NSW, 2170, Australia. AD - South Western Sydney Local Health District, Liverpool, Liverpool, NSW, 2170, Australia. AD - School of Public Health and Community Medicine, University of New South Wales Australia, Sydney, NSW, 2052, Australia. FAU - Harris, Mark F AU - Harris MF AD - Centre for Primary Health Care and Equity, University of New South Wales Australia, Sydney, NSW, 2052, Australia. FAU - Liaw, Siaw-Teng AU - Liaw ST AD - Centre for Primary Health Care and Equity, University of New South Wales Australia, Sydney, NSW, 2052, Australia. siaw@unsw.edu.au. AD - South Western Sydney Local Health District, Liverpool, Liverpool, NSW, 2170, Australia. siaw@unsw.edu.au. AD - School of Public Health and Community Medicine, University of New South Wales Australia, Sydney, NSW, 2052, Australia. siaw@unsw.edu.au. LA - eng GR - N/A/HCF Health and Medical Research Foundation/ PT - Journal Article DEP - 20190729 PL - England TA - BMC Health Serv Res JT - BMC health services research JID - 101088677 RN - 0 (Glycated Hemoglobin A) RN - 0 (hemoglobin A1c protein, human) SB - IM MH - Adult MH - Aged MH - Ambulatory Care Facilities MH - *Diabetes Mellitus, Type 2/therapy MH - Female MH - *General Practice MH - Glycated Hemoglobin/analysis MH - Hospital Information Systems MH - *Hospitalization MH - Humans MH - Male MH - *Medical Record Linkage MH - Medical Records Systems, Computerized MH - Middle Aged MH - Observation MH - Young Adult PMC - PMC6661817 OTO - NOTNLM OT - Data linkage OT - Data quality OT - Hospital admission OT - Primary care OT - Type-2 diabetes COIS- The authors declare that they have no competing interests. EDAT- 2019/07/31 06:00 MHDA- 2019/11/26 06:00 PMCR- 2019/07/29 CRDT- 2019/07/31 06:00 PHST- 2018/05/10 00:00 [received] PHST- 2019/07/10 00:00 [accepted] PHST- 2019/07/31 06:00 [entrez] PHST- 2019/07/31 06:00 [pubmed] PHST- 2019/11/26 06:00 [medline] PHST- 2019/07/29 00:00 [pmc-release] AID - 10.1186/s12913-019-4337-1 [pii] AID - 4337 [pii] AID - 10.1186/s12913-019-4337-1 [doi] PST - epublish SO - BMC Health Serv Res. 2019 Jul 29;19(1):526. doi: 10.1186/s12913-019-4337-1.