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Research ArticleMethodology

Validating the 8 CPCSSN Case Definitions for Chronic Disease Surveillance in a Primary Care Database of Electronic Health Records

Tyler Williamson, Michael E. Green, Richard Birtwhistle, Shahriar Khan, Stephanie Garies, Sabrina T. Wong, Nandini Natarajan, Donna Manca and Neil Drummond
The Annals of Family Medicine July 2014, 12 (4) 367-372; DOI: https://doi.org/10.1370/afm.1644
Tyler Williamson
1Department of Family Medicine, Queen’s University, Kingston, Ontario, Canada
2Department of Public Health Sciences, Queen’s University, Kingston, Ontario, Canada
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  • For correspondence: tylerw@cpcssn.org
Michael E. Green
1Department of Family Medicine, Queen’s University, Kingston, Ontario, Canada
2Department of Public Health Sciences, Queen’s University, Kingston, Ontario, Canada
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Richard Birtwhistle
1Department of Family Medicine, Queen’s University, Kingston, Ontario, Canada
2Department of Public Health Sciences, Queen’s University, Kingston, Ontario, Canada
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Shahriar Khan
1Department of Family Medicine, Queen’s University, Kingston, Ontario, Canada
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Stephanie Garies
3Department of Family Medicine, University of Calgary, Alberta, Canada
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Sabrina T. Wong
4School of Nursing, University of British Columbia, Vancouver, British Columbia, Canada
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Nandini Natarajan
5Department of Family Medicine, Dalhousie University, Halifax, Nova Scotia, Canada
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Donna Manca
6Department of Family Medicine, University of Alberta, Alberta, Canada
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Neil Drummond
6Department of Family Medicine, University of Alberta, Alberta, Canada
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  • CPCSSN primary care EMR data is promising for chronic disease surveillance
    Hude Quan
    Published on: 24 July 2014
  • Chronic Disease Surveillance
    Francis Lau
    Published on: 22 July 2014
  • Chronic Disease Surveillance
    James W. Mold
    Published on: 17 July 2014
  • Published on: (24 July 2014)
    Page navigation anchor for CPCSSN primary care EMR data is promising for chronic disease surveillance
    CPCSSN primary care EMR data is promising for chronic disease surveillance
    • Hude Quan, Professor

    Chronic disease surveillance requires the on-going collection of valid health information from large geographic areas. Primary data collection is time consuming and expensive, thus secondary data has been used for surveillance. Many countries routinely collect health data from hospital discharges, called hospital discharge abstract data. Such data captures sick patients and the majority of patients with chronic disease...

    Show More

    Chronic disease surveillance requires the on-going collection of valid health information from large geographic areas. Primary data collection is time consuming and expensive, thus secondary data has been used for surveillance. Many countries routinely collect health data from hospital discharges, called hospital discharge abstract data. Such data captures sick patients and the majority of patients with chronic diseases are managed or cared at out-patient settings. Thus surveillance relying on hospital discharge abstract data alone hugely underestimates disease burden. In Canada, physicians submit claims for services they provide to be remunerated regardless service locations, including inpatient, outpatient, emergency, daily surgery, homecare, etc. The submitted claims contain information on the reason for visit (i.e. diagnosis and procedure). Using the hospital discharge abstract data and physician claims, Canadian investigators reported hypertension surveillance (1) and are monitoring other chronic diseases.

    Unfortunately, the large ICD (International Classification of Disease)-coded outpatient data is not available in many countries. Electronic medical records (EMR) are potential sources for chronic disease surveillance because EMR has been widely implemented in primary care settings. The remaining technical question is how to extract the texted data from EMR and if the extracted data is valid enough to be used for surveillance. Canadian Primary Care Sentinel Surveillance Network (CPCSSN) collects data from 475 primary care practitioners, representing Canadian national primary care patients. The CPCSSN validation study led by Williamson et al. clearly demonstrated that the EMR data is valid for chronic disease surveillance. They validated eight chronic disease extraction algorithms through reviewing original EMRs and reported high statistical estimates. For example, hypertension has sensitivity of 84.9%, specificity of 93.5%, positive predictive value of 92.9% and negative predictive value of 86.0%. These values are very similar with the statistical estimates for hypertension (2), which were defined using hospital discharge abstract data and physician claims (sensitivity 75%, specificity 94%, positive predictive value 81%, negative predictive value 92%). This CPCSSN validation study is fundamental and gives researchers confidence to use the invaluable CPCSS EMR data for surveillance, health service research and epidemiological studies.

    References

    1. Robitaille C, Dai S, Waters C, Loukine L, Bancej C, Quach S, Ellison J, Campbell N, Tu K, Reimer K, Walker R, Smith M, Blais C, Quan H. Diagnosed hypertension in Canada: incidence, prevalence and associated mortality. CMAJ. 2012 Jan 10;184(1):E49-56.

    2. Quan H, Khan N, Hemmelgarn BR, Tu K, Chen G, Campbell N, Hill MD, Ghali WA, McAlister FA; Hypertension Outcome and Surveillance Team of the Canadian Hypertension Education Programs. Validation of a case definition to define hypertension using administrative data. Hypertension. 2009 Dec;54(6):1423-8.

    Competing interests: None declared

    Show Less
    Competing Interests: None declared.
  • Published on: (22 July 2014)
    Page navigation anchor for Chronic Disease Surveillance
    Chronic Disease Surveillance
    • Francis Lau, Professor

    The CPCSSN case definition validation article by Williamson et al. is a timely and much needed one that can help guide future directions for EMR related work in Canada. The case definitions can be considered foundational work that can help providers and researchers better understand how to make use of the EMR data collected.

    Yet one issue that remains unclear is the quality of the EMR data on which this validation work...

    Show More

    The CPCSSN case definition validation article by Williamson et al. is a timely and much needed one that can help guide future directions for EMR related work in Canada. The case definitions can be considered foundational work that can help providers and researchers better understand how to make use of the EMR data collected.

    Yet one issue that remains unclear is the quality of the EMR data on which this validation work is based. How much cleaning and encoding efforts are required in the original EMR systems in order to arrive at the diagnostic, medication and lab investigation codes listed in the definitions? The efforts required can influence the extent to which these definitions can be applied in routine practice as a quality improvement activity. Another issue is how to improve the quality of the EMR data so that those interested in QI can develop good data quality practice over time.

    It is clear there is still alot of work to be done in primary care EMR systems to make them relevant and useful in clinical practice. The case definition work in this article is one of the crucial steps to move this agenda forward.

    Competing interests: None declared

    Show Less
    Competing Interests: None declared.
  • Published on: (17 July 2014)
    Page navigation anchor for Chronic Disease Surveillance
    Chronic Disease Surveillance
    • James W. Mold, Professor

    The exemplary validation study published by Williamson and colleagues highlights the promise of large data sets for chronic disease surveillance. It appears to be possible, using their carefully crafted algorithms, to electronically find a majority of the diagnosed cases of some common chronic conditions without misclassifying very many patients who don't appear to have the conditions. The first practice-based researc...

    Show More

    The exemplary validation study published by Williamson and colleagues highlights the promise of large data sets for chronic disease surveillance. It appears to be possible, using their carefully crafted algorithms, to electronically find a majority of the diagnosed cases of some common chronic conditions without misclassifying very many patients who don't appear to have the conditions. The first practice-based research networks were, in fact, surveillance networks. Disease surveillance still has great appeal and potentially great value in at least some conditions (e.g. influenza). However, as pointed out in the paper, the research team was only able to identify those patients' whose chronic disease has been diagnosed and documented in the medical record. To understand the meaning of the data, one therefore needs to understand how and for what purpose diagnostic data makes its way into the record.

    Virtually every patient over the age of 75 has osteoarthritis. Those who have that diagnosis somewhere in their record either have had more symptoms, have fewer competing problems, are more likely to complain, are married to worriers, are engaged in activities that require more dexterity, have had more visits to their primary care clinician, or see a clinician who is more compulsive about identifying and documenting abnormal physical findings. For some the disease is functionally limiting, while for others it has no impact at all on their quality of life. For a few, the disease or its treatment may reduce their life expectancy. So, does knowing the prevalence of clinically documented osteoarthritis in the primary care population help us? Will it allow us to detect potential environmental etiologic contributors earlier, or will any changes detected over time be more likely to reflect changes in clinician training, patient education, visit patterns, coding and billing requirements, or cultural changes? I would think that it would be difficult to make sense of health data, which has been stripped of context and was collected and recorded for a different purpose from the one for which it is being analyzed. However, I also agree that it is very tempting to try to do so.

    Competing interests: None declared

    Show Less
    Competing Interests: None declared.
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The Annals of Family Medicine: 12 (4)
The Annals of Family Medicine: 12 (4)
Vol. 12, Issue 4
July/August 2014
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Validating the 8 CPCSSN Case Definitions for Chronic Disease Surveillance in a Primary Care Database of Electronic Health Records
Tyler Williamson, Michael E. Green, Richard Birtwhistle, Shahriar Khan, Stephanie Garies, Sabrina T. Wong, Nandini Natarajan, Donna Manca, Neil Drummond
The Annals of Family Medicine Jul 2014, 12 (4) 367-372; DOI: 10.1370/afm.1644

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Validating the 8 CPCSSN Case Definitions for Chronic Disease Surveillance in a Primary Care Database of Electronic Health Records
Tyler Williamson, Michael E. Green, Richard Birtwhistle, Shahriar Khan, Stephanie Garies, Sabrina T. Wong, Nandini Natarajan, Donna Manca, Neil Drummond
The Annals of Family Medicine Jul 2014, 12 (4) 367-372; DOI: 10.1370/afm.1644
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