Linking primary care EMR data and administrative data in Alberta, Canada:  experiences, challenges, and potential solutions

IntroductionAdministrative data are commonly used for a variety of secondary purposes. Although they lack clinical detail and risk factor information, linkage to primary care electronic medical records (EMR) could fill this gap. Primary care EMRs are a relatively new data source available in Alberta and thus, EMR-administrative linkages are novel. Objectives and ApproachTo describe the process undertaken for linking de-identified primary care EMR data from two regional Alberta networks of the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) with administrative data (hospital admissions, emergency department visits, pharmacy information) from Alberta Health Services Analytics, specifically as it relates to a study on patients with complex, chronic diseases. As this linkage process is new in Alberta, we will describe the challenges encountered and possible solutions to inform future data linkage for research studies. ResultsLinkage steps: 1) approval from research ethics board and individual CPCSSN providers as data custodians; 2) notify Privacy Commissioner on behalf of custodian; 3) send linking key (CPCSSN patient ID, EMR ID) from regional database to Analytics; 4) send linking files (patient personal health number [PHN], EMR ID) from custodian’s EMR system to Analytics; 5) match unique EMR ID from linking key and clinic linking files; 6) PHN from clinic linking file mapped to administrative data; 7) data de-identified before transferring to secure repository; administrative data matched to EMR data using CPCSSN ID. Challenges: obtaining individual provider consent for each study; sampling bias; delays/issues generating clinic linkage file; mismatch between patients in clinic \& regional linking files. Current and potential solutions will be discussed during the presentation. Conclusion/ImplicationsAs primary care EMR and administrative data become more routinely linked and accepted, the process will become more efficient and streamlined. These data will contribute to a better understanding of patients and their care in Alberta.

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Identifying Prenatal Opioid Exposure in Health Administrative Data for Public Health Surveillance and Epidemiologic Research

International Journal for Population Data Science ◽

10.23889/ijpds.v5i5.1453 ◽

2020 ◽

Vol 5 (5) ◽

Author(s):

Andi Camden ◽

Teresa To ◽

Joel G Ray ◽

Tara Gomes ◽

Li Bai ◽

...

Keyword(s):

Prescription Drug ◽

Administrative Data ◽

Population Based ◽

Emergency Department Visits ◽

Prescription Data ◽

Opioid Use ◽

Health Administrative Data ◽

Maternal Characteristics ◽

Data Source ◽

The Impact

IntroductionAccurate estimation of prenatal opioid exposure (POE) is needed for population-based surveillance & research but can be challenging with health administrative data due to varying definitions & methods. Prior research has relied primarily on infant records with a diagnosis of neonatal abstinence syndrome (NAS). Objectives and Approach1) Evaluate the impact of using different definitions of maternal opioid use in the estimation of POE; 2) Investigate whether maternal characteristics vary by the type of definition used. Population-based cross-sectional study of all hospital births (N= 454,746) from 2014-2017 in Ontario, Canada. Multiple linked population-based health administrative databases were used to identify opioid-related pre- & perinatal Emergency Department visits & hospitalizations & opioid prescriptions. We examined how pre-conception & in-pregnancy maternal characteristics varied by using different approaches to ascertain POE. ResultsThere were 9624 live/still births with POE. Ascertainment of POE was highest using maternal prescription drug data (79%) & infant hospital records with NAS (45%). Maternal characteristics varied by data source used for POE ascertainment. Opioid-related health care during pregnancy identified a high-risk phenotype, contrasted with those ascertained through prescription data, with respective rates of 64% vs. 54% for social assistance, 37% vs. 12% for polydrug use, 23% vs. 6% for alcohol use, 26% vs. 19% for 3+ live births, 13% vs. 5% for victim of violence, 12% vs. 6% for involvement in criminal justice system & 64% vs. 17% for mental health & addictions hospital care. Conclusion / ImplicationsPOE ascertainment differs by health administrative data source & ability to link both across maternal records and with infant. Prescription drug data identified the highest number of opioid-exposed births and, with linked healthcare records, is useful to identify illicit opioid use & additional risk factors. Clinically meaningful differences in maternal characteristics of opioid users exist by POE ascertainment method.

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Multicomponent interventions for enhancing primary care: a systematic review

British Journal of General Practice ◽

10.3399/bjgp20x714199 ◽

2020 ◽

Vol 71 (702) ◽

pp. e10-e21

Author(s):

Geronimo Jimenez ◽

David Matchar ◽

Gerald Choon-Huat Koh ◽

Josip Car

Keyword(s):

Systematic Review ◽

Primary Care ◽

Hospital Admissions ◽

Grey Literature ◽

Emergency Department Visits ◽

Narrative Form ◽

Provider Satisfaction ◽

Quadruple Aim ◽

Literature Searches ◽

Multicomponent Interventions

BackgroundMany countries have implemented interventions to enhance primary care to strengthen their health systems. These programmes vary widely in features included and their impact on outcomes.AimTo identify multiple-feature interventions aimed at enhancing primary care and their effects on measures of system success — that is, population health, healthcare costs and utilisation, patient satisfaction, and provider satisfaction (quadruple-aim outcomes).Design and settingSystematic review and narrative synthesis.MethodElectronic, manual, and grey-literature searches were performed for articles describing multicomponent primary care interventions, providing details of their innovation features, relationship to the ‘4Cs’ (first contact, comprehensiveness, coordination, and continuity), and impact on quadruple-aim outcomes. After abstract and full-text screening, articles were selected and their quality appraised. Results were synthesised in a narrative form.ResultsFrom 37 included articles, most interventions aimed to improve access, enhance incentives for providers, provide team-based care, and introduce technologies. The most consistent improvements related to increased primary care visits and screening/preventive services, and improved patient and provider satisfaction; mixed results were found for hospital admissions, emergency department visits, and expenditures. The available data were not sufficient to link interventions, achievement of the 4Cs, and outcomes.ConclusionMost analysed interventions improved some aspects of primary care while, simultaneously, producing non-statistically significant impacts, depending on the features of the interventions, the measured outcome(s), and the populations being studied. A critical research gap was revealed, namely, in terms of which intervention features to enhance primary care (alone or in combination) produce the most consistent benefits.

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Interventions in primary care for reducing preventable medication errors that lead to hospital admissions, mortality and emergency department visits

10.1002/14651858.cd003942 ◽

2002 ◽

Cited By ~ 1

Author(s):

Hanan Khalil ◽

Anthony J Avery ◽

Helen Chambers ◽

Brian Bell ◽

Brian Serumaga ◽

...

Keyword(s):

Primary Care ◽

Emergency Department ◽

Medication Errors ◽

Hospital Admissions ◽

Emergency Department Visits

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Primary care EMR and administrative data linkage in Alberta, Canada: describing the suitability for hypertension surveillance

BMJ Health & Care Informatics ◽

10.1136/bmjhci-2020-100161 ◽

2020 ◽

Vol 27 (3) ◽

pp. e100161

Author(s):

Stephanie Garies ◽

Erik Youngson ◽

Boglarka Soos ◽

Brian Forst ◽

Kimberley Duerksen ◽

...

Keyword(s):

Primary Care ◽

Administrative Data ◽

Rural Areas ◽

Data Linkage ◽

Population Level ◽

Primary Care Providers ◽

Sex Characteristics ◽

Care Providers ◽

Surveillance Network ◽

Discharge Database

ObjectiveTo describe the process for linking electronic medical record (EMR) and administrative data in Alberta and examine the advantages and limitations of utilising linked data for hypertension surveillance.MethodsDe-identified EMR data from 323 primary care providers contributing to the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) in Alberta were used. Mapping files from each contributing provider were generated from their EMR to facilitate linkage to administrative data within the provincial health data warehouse. Deterministic linkage was conducted using valid personal healthcare number (PHN) with age and/or sex. Characteristics of patients and providers in the linked cohort were compared with population-level sources. Criteria used to define hypertension in both sources were examined.ResultsData were successfully linked for 6307 hypertensive patients (96.2% of eligible patients) from 49 contributing providers. Non-linkages from invalid PHN (n=246) occurred more for deceased patients and those with fewer primary care encounters, with differences due to type of EMR and patient EMR status. The linked cohort had more patients who were female, >60 years and residing in rural areas compared to the provincial healthcare registry. Family physicians were more often female and medically trained in Canada compared to all physicians in Alberta. Most patients (>97%) had ≥1 record in the registry, pharmacy, emergency/ambulatory care and claims databases; 44.3% had ≥1 record in the hospital discharge database.ConclusionEMR-administrative data linkage has the potential to enhance hypertension surveillance. The current linkage process in Alberta is limited and subject to selection bias. Processes to address these deficiencies are under way.

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Interventions in primary care for reducing preventable medication errors that lead to hospital admissions, mortality and emergency department visits

10.1002/14651858.cd003942.pub2 ◽

2013 ◽

Author(s):

Hanan Khalil ◽

Anthony J Avery ◽

Helen Chambers ◽

Brian Bell ◽

Brian Serumaga ◽

...

Keyword(s):

Primary Care ◽

Emergency Department ◽

Medication Errors ◽

Hospital Admissions ◽

Emergency Department Visits

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Can Linked Electronic Medical Record and Administrative Data Help Us Identify Those Living With Frailty?

International Journal for Population Data Science ◽

10.23889/ijpds.v3i4.829 ◽

2018 ◽

Vol 3 (4) ◽

Author(s):

Sabrina Wong ◽

Alan Katz ◽

Tyler Williamson ◽

Sandra Peterson ◽

Carole Taylor ◽

...

Keyword(s):

Primary Care ◽

Medical Record ◽

Electronic Medical Record ◽

Administrative Data ◽

Functional Decline ◽

Improve Patient Care ◽

Population Data ◽

Population Level ◽

Surveillance Network ◽

Clinical Indicators

IntroductionFrailty is a combination of factors that increase vulnerability to functional decline, dependence and/or death. Frailty cannot easily be defined by comorbidities or medical treatment alone. Accurate detection of frailty in practice and at a population level is needed. This may be achieved using a combination of data sources. Objectives and ApproachWe construct algorithms that can identify frailty using electronic medical record (EMR) and administrative data. We linked EMR data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) nodes and the administrative (e.g. billings, hospitalizations) from Population Data BC and the Manitoba Health Policy Centre. Frailty was defined as individuals 65+ who were receiving home services, had specific chronic conditions, received specific diagnoses, and/or had specific lab or other clinical indicators. We describe sociodemographic characteristics, risk factors, prescribed medications, use and costs of healthcare for those identified as frail. ResultsPeople were identified as frail in 2014 and all analysis was completed with 2015 data. Among those who were > 65 years, who had a record in both EMR and administrative data, 5\%-8\% (n=191 of 3,553, BC; n=2,396 of 29,382, MB) were identified as frail. There was a higher likelihood of being frail with increasing age and being a woman. In BC, those identified as frail have higher contacts with primary care (n=20 vs. n=10) and more days in hospital (n=7.4 vs. n=2.0) compared to those who are not frail. Twenty two percent of those identified as frail in 2014 died in 2015, compared to a mortality rate of 2\% among those who are not frail. Conclusion/ImplicationsIdentifying and reporting on those who are frail in primary care as well as in communities could enable targeted communications with patients and families and community based resources in order to improve patient care, patients’ and caregivers’ quality of life and better use of the healthcare system.

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The effect of provider affiliation with a primary care network on emergency department visits and hospital admissions

Canadian Medical Association Journal ◽

10.1503/cmaj.170385 ◽

2018 ◽

Vol 190 (10) ◽

pp. E276-E284 ◽

Cited By ~ 6

Author(s):

Finlay A. McAlister ◽

Jeffrey A. Bakal ◽

Lee Green ◽

Brad Bahler ◽

Richard Lewanczuk

Keyword(s):

Primary Care ◽

Emergency Department ◽

Hospital Admissions ◽

Emergency Department Visits ◽

Care Network

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Validation of dementia coding in hospital admissions, death and primary care data

International Journal for Population Data Science ◽

10.23889/ijpds.v1i1.100 ◽

2017 ◽

Vol 1 (1) ◽

Author(s):

Tim Wilkinson ◽

Christian Schnier ◽

Amanda Ly ◽

Cathie Sudlow

Keyword(s):

Primary Care ◽

Hospital Admissions ◽

Large Population ◽

Population Based ◽

Data Sources ◽

Health Concern ◽

Case Record ◽

Primary Care Data ◽

Data Source

ABSTRACT ObjectivesDementia is a major public health concern worldwide and consequently there is an urgent need to expedite research into its causes so that preventative strategies can be sought. Given that dementia is likely to be the result of a complex interplay of many factors, large study populations are required in order to detect effects reliably. UK Biobank (UKB) is a large, population-based, prospective cohort study of over 503,000 participants aged 40-69 years when recruited between 2006 and 2010. Participant follow up is chiefly via linkage to routinely-collected health datasets such as hospital admissions, death registrations and increasingly, to primary care data. In this pilot study we sought to estimate the accuracy of using these routine data sources to identify dementia outcomes in UKB participants. ApproachWe created a list of ICD-10 and primary care (Read version 2) dementia codes, with the intention of maximising positive predictive value (PPV) over sensitivity. We identified UKB participants who were recruited in Edinburgh and had at least one dementia code in any of the three data sources. We searched the NHS Lothian electronic medical record (EMR) for each participant and extracted all relevant letters and investigation results. Participants were excluded if no EMR entry for that patient could be found. A neurologist adjudicated on whether dementia was present or not based on the extracted case record, providing the reference standard to which the coded data were compared. The PPV was then calculated for each data source individually and combined. A subgroup analysis was performed on participants who had a dementia code across more than one dataset. ResultsAmong 17,000 Edinburgh-based participants (median age 57 years at recruitment in 2007/8), hospital and death data were available to 2012 with primary care data for 12,000 to 2013. 46 participants had a dementia code in at least one data source. 44 of these had available EMR data. PPVs for dementia were 41/44 (93%, 95% CI 81-99) overall, 13/15 (87%, 95% CI 60-98) for hospital admissions, 2/2 (100%, 95% CI 16-100) for death registrations, 33/34 (97%, 95% CI 85-100) for primary care, and 7/7 (100%, 95% CI 59-100) for participants with codes in ≥2 datasets. ConclusionRoutinely-collected health data may be sufficiently accurate to identify dementia outcomes in UK population-based cohorts. We plan to extend this study to longer follow-up times and other regions to increase sample size, investigate dementia subtypes and assess generalisability.

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