Patterns of care for people presenting to Australian general practice with musculoskeletal complaints based on routinely collected data: protocol for an observational cohort study using the Population Level Analysis and Reporting (POLAR) database

IntroductionGeneral practice is integral to the Australian healthcare system. Outcome Health’s POpulation Level Analysis and Reporting (POLAR) database uses de-identified electronic health records to analyse general practice data in Australia. Previous studies using routinely collected health data for research have not consistently reported the codes and algorithms used to describe the population, exposures, interventions and outcomes in sufficient detail to allow replication. This paper reports a study protocol investigating patterns of care for people presenting with musculoskeletal conditions to general practice in Victoria, Australia. Its focus is on the systematic approach used to classify and select eligible records from the POLAR database to facilitate replication. This will be useful for other researchers using routinely collected health data for research.Methods and analysisThis is a retrospective cohort study. Patient-related data will be obtained through electronic health records from a subset of general practices across three primary health networks (PHN) in southeastern Victoria. Data for patients with a low back, neck, shoulder and/or knee condition and who received at least one general practitioner (GP) face-to-face consultation between 1 January 2014 and 31 December 2018 will be included. Data quality checks will be conducted to exclude patients with poor data recording and/or non-continuous follow-up. Relational data files with eligible and valid records will be merged to select the study cohort and the GP care received (consultations, imaging requests, prescriptions and referrals) between diagnosis and 31 December 2018. Number and characteristics of patients and GPs, and number, type and timing of imaging requests, prescriptions for pain relief and referrals to other health providers will be investigated.Ethics and disseminationEthics approval was obtained from the Cabrini and Monash University Human Research Ethics Committees (Reference Numbers 02-21-01-19 and 16975, respectively). Study findings will be reported to Outcome Health, participating PHNs, disseminated in academic journals and presented in conferences.

Download Full-text

Methods for enhancing the reproducibility of clinical epidemiology research in linked electronic health records: results and lessons learned from the CALIBER platform

International Journal for Population Data Science ◽

10.23889/ijpds.v1i1.84 ◽

2017 ◽

Vol 1 (1) ◽

Cited By ~ 2

Author(s):

Spiros Denaxas ◽

Holger Kunz ◽

Liam Smeeth ◽

Arturo Gonzalez-Izquierdo ◽

Harry Boutselakis ◽

...

Keyword(s):

Statistical Analysis ◽

Electronic Health Records ◽

Life Course ◽

Scientific Community ◽

Health Data ◽

Data Curation ◽

Raw Data ◽

Health Records ◽

Routinely Collected Health Data ◽

Electronic Health

ABSTRACTObjectivesElectronic health records (EHR) across primary, secondary, and tertiary care are increasingly being linked for research at a population level. The increasing volume, variety, velocity, and veracity of big biomedical data makes research reproducibility challenging. Research reproducibility and replicability is essential for the external validity and generalizability of scientific findings and the lack of standardized approaches and tools and relative opaqueness of data manipulation methods is detrimental to their integrity. The objective of this study was to explore, evaluate and propose methods, tools and approaches for addressing some of the challenges associated with reproducibility when using linked national electronic health records for research. ApproachWe systematically searched literature and internet resources for well-established and appropriate methods, tools, and approaches used in related scientific disciplines. The identified techniques were systematically evaluated in terms of their capacity to facilitate reproducible research in routinely collected health data across the life course of a research project: from protocol creation and raw data curation to data transformation and statistical analysis though to finding dissemination and impact. Most importantly, the identified techniques were tested and applied in a contemporary database of linked electronic health records. CALIBER is a research data platform of linked national electronic health records from primary care (Clinical Practice Research Datalink), secondary care (Hospital Episode Statistics), acute coronary syndrome disease registry (Myocardial Ischaemia National Audit Project) and cause-specific mortality (Office for National Statistics) for roughly 2 million adults. ResultsFirstly, we present the review of methods and approaches which we identified through our search. Secondly, we propose a set of recommendations for applying them within the context of research projects making use of linked routinely collected health data. Focal interests included: a) documentation of data (attributes, relationships, and interpretation), b) data processing (source code, instructions, and parameters), c) results (visualizations, figures), and any supplementary material. Thirdly, we present approaches around a) raw data curation using international metadata standards, b) study protocol encoding, c) provenance and sharing of data transformation and statistical analysis operations, d) public and private data retention, and e) computable EHR-driven phenotypes. ConclusionThe complexity and size of routinely collected health data is increasing through linkages across distributed data sources. The scientific community benefits from findings which can be replicated. This study presents a number of methods, tools and approaches across the project life course for ensuring that their research studies are reproducible and replicable from the wider scientific community.

Download Full-text

Knowledge Driven Phenotyping

10.1101/19013748 ◽

2019 ◽

Author(s):

Honghan Wu ◽

Minhong Wang ◽

Qianyi Zeng ◽

Wenjun Chen ◽

Jeff Z. Pan ◽

...

Keyword(s):

Electronic Health Records ◽

Heterogeneous Data ◽

Health Data ◽

Data Sources ◽

Health Records ◽

Knowledge And Skills ◽

Preliminary Results ◽

Routinely Collected Health Data ◽

Electronic Health

AbstractExtracting patient phenotypes from routinely collected health data (such as Electronic Health Records) requires translating clinically-sound phenotype definitions into queries/computations executable on the underlying data sources by clinical researchers. This requires significant knowledge and skills to deal with heterogeneous and often imperfect data. Translations are time-consuming, error-prone and, most importantly, hard to share and reproduce across different settings. This paper proposes a knowledge driven framework that (1) decouples the specification of phenotype semantics from underlying data sources; (2) can automatically populate and conduct phenotype computations on heterogeneous data spaces. We report preliminary results of deploying this framework on five Scottish health datasets.

Download Full-text

Incidence, risk factors, and health service burden of sequelae of campylobacter and non-typhoidal salmonella infections in England, 2000–2015: A retrospective cohort study using linked electronic health records

Journal of Infection ◽

10.1016/j.jinf.2020.05.027 ◽

2020 ◽

Vol 81 (2) ◽

pp. 221-230 ◽

Cited By ~ 1

Author(s):

Oluwaseun B. Esan ◽

Rafael Perera ◽

Noel McCarthy ◽

Mara Violato ◽

Thomas R. Fanshawe

Keyword(s):

Risk Factors ◽

Cohort Study ◽

Health Service ◽

Electronic Health Records ◽

Retrospective Cohort Study ◽

Retrospective Cohort ◽

Health Records ◽

Salmonella Infections ◽

Incidence Risk ◽

Electronic Health

Download Full-text

The process of sourcing and preparing electronic health records data to implement a machine-learning algorithm for early identification of maternal cardiovascular risk (Preprint)

10.2196/preprints.34932 ◽

2021 ◽

Author(s):

Nawar Shara ◽

Kelley M. Anderson ◽

Noor Falah ◽

Maryam F. Ahmad ◽

Darya Tavazoei ◽

...

Keyword(s):

Machine Learning ◽

Cardiovascular Risk ◽

Electronic Health Records ◽

Patient Care ◽

Early Identification ◽

Health Data ◽

Patient Specific ◽

Patient Records ◽

Health Records ◽

Electronic Health

BACKGROUND Healthcare data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes [6]. However, the differences that exist in each individual’s health records, combined with the lack of health-data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. While these problems exist throughout healthcare, they are especially prevalent within maternal health, and exacerbate the maternal morbidity and mortality (MMM) crisis in the United States. OBJECTIVE Maternal patient records were extracted from the electronic health records (EHRs) of a large tertiary healthcare system and made into patient-specific, complete datasets through a systematic method so that a machine-learning-based (ML-based) risk-assessment algorithm could effectively identify maternal cardiovascular risk prior to evidence of diagnosis or intervention within the patient’s record. METHODS We outline the effort that was required to define the specifications of the computational systems, the dataset, and access to relevant systems, while ensuring data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for its use by a proprietary risk-stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions. RESULTS Patient records can be made actionable for the goal of effectively employing machine learning (ML), specifically to identify cardiovascular risk in pregnant patients. CONCLUSIONS Upon acquiring data, including the concatenation, anonymization, and normalization of said data across multiple EHRs, the use of a machine-learning-based (ML-based) tool can provide early identification of cardiovascular risk in pregnant patients. CLINICALTRIAL N/A

Download Full-text