Leveraging EHR Data for Outcomes and Comparative Effectiveness Research in Oncology

Manion, Frank J.; Harris, Marcelline R.; Büyüktür, Ayşe G.; Clark, Patricia; An, Lawrence C.; Hanauer, David A.

doi:10.1007/s11912-012-0272-6

Cited by 25 publications

(19 citation statements)

References 27 publications

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“…Much of the clinical information in EHRs is recorded in free text fields. Free text is often richer in detail but requires specialized approaches, including NLP, to extract clinically relevant concepts [ 15 ]. Such techniques have been applied in the C&I Research Database enabling the retrieval of information from free text.…”

Section: Discussionmentioning

confidence: 99%

The Camden & Islington Research Database: Using electronic mental health records for research

et al. 2018

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BackgroundElectronic health records (EHRs) are widely used in mental health services. Case registers using EHRs from secondary mental healthcare have the potential to deliver large-scale projects evaluating mental health outcomes in real-world clinical populations.MethodsWe describe the Camden and Islington NHS Foundation Trust (C&I) Research Database which uses the Clinical Record Interactive Search (CRIS) tool to extract and de-identify routinely collected clinical information from a large UK provider of secondary mental healthcare, and demonstrate its capabilities to answer a clinical research question regarding time to diagnosis and treatment of bipolar disorder.ResultsThe C&I Research Database contains records from 108,168 mental health patients, of which 23,538 were receiving active care. The characteristics of the patient population are compared to those of the catchment area, of London, and of England as a whole. The median time to diagnosis of bipolar disorder was 76 days (interquartile range: 17–391) and median time to treatment was 37 days (interquartile range: 5–194). Compulsory admission under the UK Mental Health Act was associated with shorter intervals to diagnosis and treatment. Prior diagnoses of other psychiatric disorders were associated with longer intervals to diagnosis, though prior diagnoses of schizophrenia and related disorders were associated with decreased time to treatment.ConclusionsThe CRIS tool, developed by the South London and Maudsley NHS Foundation Trust (SLaM) Biomedical Research Centre (BRC), functioned very well at C&I. It is reassuring that data from different organizations deliver similar results, and that applications developed in one Trust can then be successfully deployed in another. The information can be retrieved in a quicker and more efficient fashion than more traditional methods of health research. The findings support the secondary use of EHRs for large-scale mental health research in naturalistic samples and settings investigated across large, diverse geographical areas.

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Section: Discussionmentioning

confidence: 99%

The Camden & Islington Research Database: Using electronic mental health records for research

et al. 2018

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“…To date, frameworks documenting variations in clinic data workflows are scarce 17 and have mostly been used to standardize data entry within a given organization. Even this use of workflows has been hindered by EHR platforms that contain unstructured data 18 or that do not allow electronic data transfer from external organizations. 19 Our methods build on the work of Johnson et al that emphasizes the importance of understanding the context in which data are collected and used (referred to as the "data provenance").…”

Section: Malhoutra Et Almentioning

confidence: 99%

Mapping Multi-Site Clinic Workflows to Design Systems-Enabled Interventions

Coronado¹,

Retecki²,

Petrik³

et al. 2017

eGEMs

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Objective: Variations in processes for different clinics and health systems can dramatically change the way preventive interventions are implemented. We present a method for documenting these variations using workflow diagrams and demonstrate how understanding workflow aided an electronic health record (EHR) embedded colorectal cancer screening intervention. Materials and Methods:We mapped variation in processes for ordering and documenting fecal testing, current colonoscopy, prior colonoscopies, and pathology results. This work was part of a multi-site cluster-randomized pragmatic trial to test a mailed approach to offering fecal testing at 26 safety net clinics (in eight organizations) in Oregon and Northern California. We created clinic-specific workflow diagrams and then distilled them into consolidated diagrams that captured the variations.Results: Clinics had varied practices for storing and using information about colorectal cancer screening. Developing workflow diagrams of key processes enabled clinics to find optimal ways to send fecal test kits to patients due for screening. The workflows informed the rollout of new EHR tools and identified best practices for data capture.Discussion: Diagramming workflows can have great utility when implementing and refining EHR tools for clinical practice, especially when doing so across multiple clinical sites. The process of developing the workflows uncovered successful practice recommendations and revealed limitations and potential effects of a research intervention. Conclusion:Our method of documenting clinical process variation might inform other EHR-powered, multi-site research and can improve data feedback from EHR systems to clinical caregivers.

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“… 4 , 5 Additionally, there are published opinion pieces highlighting the potential importance the use of EHRs in research. 6 …”

Section: Introductionmentioning

confidence: 99%

Diabetes and the direct secondary use of electronic health records: Using routinely collected and stored data to drive research and understanding

et al. 2018

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IntroductionElectronic health records provide an unparalleled opportunity for the use of patient data that is routinely collected and stored, in order to drive research and develop an epidemiological understanding of disease. Diabetes, in particular, stands to benefit, being a data-rich, chronic-disease state. This article aims to provide an understanding of the extent to which the healthcare sector is using routinely collected and stored data to inform research and epidemiological understanding of diabetes mellitus.MethodsNarrative literature review of articles, published in both the medical- and engineering-based informatics literature.ResultsThere has been a significant increase in the number of papers published, which utilise electronic health records as a direct data source for diabetes research. These articles consider a diverse range of research questions. Internationally, the secondary use of electronic health records, as a research tool, is most prominent in the USA. The barriers most commonly described in research studies include missing values and misclassification, alongside challenges of establishing the generalisability of results.DiscussionElectronic health record research is an important and expanding area of healthcare research. Much of the research output remains in the form of conference abstracts and proceedings, rather than journal articles. There is enormous opportunity within the United Kingdom to develop these research methodologies, due to national patient identifiers. Such a healthcare context may enable UK researchers to overcome many of the barriers encountered elsewhere and thus to truly unlock the potential of electronic health records.

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Leveraging EHR Data for Outcomes and Comparative Effectiveness Research in Oncology

Cited by 25 publications

References 27 publications

The Camden & Islington Research Database: Using electronic mental health records for research

The Camden & Islington Research Database: Using electronic mental health records for research

Mapping Multi-Site Clinic Workflows to Design Systems-Enabled Interventions

Diabetes and the direct secondary use of electronic health records: Using routinely collected and stored data to drive research and understanding

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