Australian Journal of Primary Health Australian Journal of Primary Health Society
The issues influencing community health services and primary health care
RESEARCH ARTICLE (Open Access)

Consistency of denominator data in electronic health records in Australian primary healthcare services: enhancing data quality

Ross Bailie A B , Jodie Bailie A , Amal Chakraborty A and Kevin Swift A
+ Author Affiliations
- Author Affiliations

A Centre for Primary Health Care Systems, Menzies School of Health Research, Charles Darwin University, PO Box 10639, Adelaide Street, Brisbane, Qld 4000, Australia.

B Corresponding author. Email: ross.bailie@menzies.edu.au

Australian Journal of Primary Health 21(4) 450-459 https://doi.org/10.1071/PY14071
Submitted: 25 April 2014  Accepted: 15 September 2014   Published: 28 October 2014

Abstract

The quality of data derived from primary healthcare electronic systems has been subjected to little critical systematic analysis, especially in relation to the purported benefits and substantial investment in electronic information systems in primary care. Many indicators of quality of care are based on numbers of certain types of patients as denominators. Consistency of denominator data is vital for comparison of indicators over time and between services. This paper examines the consistency of denominator data extracted from electronic health records (EHRs) for monitoring of access and quality of primary health care. Data collection and analysis were conducted as part of a prospective mixed-methods formative evaluation of the Commonwealth Government’s Indigenous Chronic Disease Package. Twenty-six general practices and 14 Aboriginal Health Services (AHSs) located in all Australian States and Territories and in urban, regional and remote locations were purposively selected within geographically defined locations. Percentage change in reported number of regular patients in general practices ranged between –50% and 453% (average 37%). The corresponding figure for AHSs was 1% to 217% (average 31%). In approximately half of general practices and AHSs, the change was ≥20%. There were similarly large changes in reported numbers of patients with a diagnosis of diabetes or coronary heart disease (CHD), and Indigenous patients. Inconsistencies in reported numbers were due primarily to limited capability of staff in many general practices and AHSs to accurately enter, manage, and extract data from EHRs. The inconsistencies in data required for the calculation of many key indicators of access and quality of care places serious constraints on the meaningful use of data extracted from EHRs. There is a need for greater attention to quality of denominator data in order to realise the potential benefits of EHRs for patient care, service planning, improvement, and policy. We propose a quality improvement approach for enhancing data quality.

Additional keywords: clinical information systems, electronic data extraction, primary health care, quality indicators, quality of data.


References

Bailie R, Griffin J, Kelaher M, McNeair T, Percival N, Laycock A, Shierhout G (2013a) Menzies School of Health Research for the Australian Government Department of Health and Ageing. Sentinel sites evaluation: final report: Menzies School of Health Research, February 2013. Commonwealth of Australia, Canberra.

Bailie R, Griffin J, Kelaher M, McNeair T, Percival N, Laycock A, Shierhout G (2013b) Sentinel sites evaluation: final report – appendices: Menzies School of Health Research, February 2013. Commonwealth of Australia, Canberra.

Barkhuysen P, de Grauw W, Akkermans R, Donkers J, Schers H, Biermans M (2014) Is the quality of data in an electronic medical record sufficient for assessing the quality of primary care? Journal of the American Medical Informatics Association 21, 692–698.
Is the quality of data in an electronic medical record sufficient for assessing the quality of primary care?CrossRef | 24145818PubMed | open url image1

Black A, Car J, Pagliari C, Anandan C, Cresswell K, Bokun T, McKinstry B, Procter R, Majeed A, Sheikh A (2011) The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Medicine 8, e1000387
The impact of eHealth on the quality and safety of health care: a systematic overview.CrossRef | 21267058PubMed | open url image1

Coiera E (2013) Why e-health is so hard. The Medical Journal of Australia 198, 178–179.
Why e-health is so hard.CrossRef | 23451947PubMed | open url image1

Crawford B, Skeath M, Whippy A (2013) Multifocal clinical performance improvement across 21 hospitals. Journal for Healthcare Quality
Multifocal clinical performance improvement across 21 hospitals.CrossRef | 24001267PubMed | open url image1

Crosson JC, Ohman-Strickland PA, Cohen DJ, Clark EC, Crabtree BF (2012) Typical electronic health record use in primary care practices and the quality of diabetes care. Annals of Family Medicine 10, 221–227.
Typical electronic health record use in primary care practices and the quality of diabetes care.CrossRef | 22585886PubMed | open url image1

Denham CR, Classen DC, Swenson SJ, Henderson MJ, Zeltner T, Bates DW (2013) Safe use of electronic health records and health information technology systems: trust but verify. Journal of Patient Safety 9, 177–189.
Safe use of electronic health records and health information technology systems: trust but verify.CrossRef | 24257062PubMed | open url image1

Greiver M, Barnsley J, Glazier R, Harvey BJ, Moineddin R (2012) Measuring data reliability for preventive services in electronic medical records. BMC Health Services Research 12, 116
Measuring data reliability for preventive services in electronic medical records.CrossRef | 22583552PubMed | open url image1

Kaplan HC, Provost LP, Froehle CM, Margolis PA (2012) The Model for Understanding Success in Quality (MUSIQ): building a theory of context in healthcare quality improvement. BMJ Quality & Safety. 21, 13–20.
The Model for Understanding Success in Quality (MUSIQ): building a theory of context in healthcare quality improvement.CrossRef | open url image1

Kelly J, Schattner P, Sims J (2009) Are general practice networks ‘ready’ for clinical data management? Australian Family Physician 38, 1007–1010.

Klazinga N, Fischer C, Asbroek A (2011) Health services research related to performance indicators and benchmarking in Europe. Journal of Health Services Research & Policy 16, 38–47.
Health services research related to performance indicators and benchmarking in Europe.CrossRef | open url image1

Lau F, Price M, Boyd J, Partridge C, Bell H, Raworth R (2012) Impact of electronic medical record on physician practice in office settings: a systematic review. BMC Medical Informatics and Decision Making 12, 10
Impact of electronic medical record on physician practice in office settings: a systematic review.CrossRef | 22364529PubMed | open url image1

Liljeqvist GTH, Staff M, Puech M, Blom H, Torvaldsen S (2011) Automated data extraction from general practice records in an Australian setting: trends in influenza-like illness in sentinel general practices and emergency departments. BMC Public Health 11, 435
Automated data extraction from general practice records in an Australian setting: trends in influenza-like illness in sentinel general practices and emergency departments.CrossRef | open url image1

Lynott MH, Kooienga SA, Stewart VT (2012) Communication and the electronic health record training: a comparison of three healthcare systems. Informatics in Primary Care 20, 7–12.
Communication and the electronic health record training: a comparison of three healthcare systems.CrossRef | 23336831PubMed | open url image1

Maddocks H, Stewart M, Thind A, Terry AL, Chevendra V, Marshall JN, Denomme LB, Cejic S (2011) Feedback and training tool to improve provision of preventive care by physicians using EMRs: a randomised control trial. Informatics in Primary Care 19, 147–153.

Majeed A, Car J, Sheikh A (2008) Accuracy and completeness of electronic patient records in primary care. Family Practice 25, 213–214.
Accuracy and completeness of electronic patient records in primary care.CrossRef | 18694896PubMed | open url image1

Parsons A, McCullough C, Wang J, Shih S (2012) Validity of electronic health record-derived quality measurement for performance monitoring. Journal of the American Medical Informatics Association 19, 604–609.
Validity of electronic health record-derived quality measurement for performance monitoring.CrossRef | 22249967PubMed | open url image1

Peiris D, Agaliotis M, Patel B, Patel A (2013) Validation of a general practice audit and data extraction tool. Australian Family Physician 42, 816–819.

Riley WJ, Parsons HM, Duffy GL, Moran JW, Henry B (2010) Realizing transformational change through quality improvement in public health. Journal of Public Health Management and Practice 16, 72–78.
Realizing transformational change through quality improvement in public health.CrossRef | 20009648PubMed | open url image1

Schattner P, Saunders M, Stanger L, Speak M, Russo K (2011) Data extraction and feedback – does this lead to change in patient care? Australian Family Physician 40, 623–628.

Spitzer R (2009) Clinical information and sociotechnology. Nurse Leader 7, 6–8.
Clinical information and sociotechnology.CrossRef | open url image1

Thiru K, Hassey A, Sullivan F (2003) Systematic review of scope and quality of electronic patient record data in primary care. BMJ 326, 1070
Systematic review of scope and quality of electronic patient record data in primary care.CrossRef | 12750210PubMed | open url image1

Urbis (2010) Indigenous chronic disease package monitoring and evaluation framework [updated 17 December 2010]. Available at http://www.health.gov.au/internet/ctg/publishing.nsf/Content/ICDP-monitoring-and-evaluation-framework [Verified 9 March 2014]

World Health Organization (2003) Improving data quality: a guide for developing countries. World Health Organization, Manila.


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