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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2020

Open Access 01-12-2020 | Care | Research article

Use of online knowledge base in primary health care and correlation to health care quality: an observational study

Authors: Christian Gerdesköld, Eva Toth-Pal, Inger Wårdh, Gunnar H. Nilsson, Anna Nager

Published in: BMC Medical Informatics and Decision Making | Issue 1/2020

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Abstract

Background

Evidence-based information available at the point of care improves patient care outcomes. Online knowledge bases can increase the application of evidence-based medicine and influence patient outcome data which may be captured in quality registries. The aim of this study was to explore the effect of use of an online knowledge base on patient experiences and health care quality.

Methods

The study was conducted as a retrospective, observational study of 24 primary health care centers in Sweden exploring their use of an online knowledge base. Frequency of use was compared to patient outcomes in two national quality registries. A socio-economic Care Need Index was applied to assess whether the burden of care influenced the results from those quality registries. Non-parametric statistical methods and linear regression were used.

Results

Frequency of knowledge base use showed two groups: frequent and non-frequent users, with a significant use difference between the groups (p < 0.001). Outcome data showed significant higher values for all seven National Primary Care Patient Survey dimensions in the frequent compared to the non-frequent knowledge base users (p < 0.001), whereas 10 out of 11 parameters in the National Diabetes Register showed no differences between the groups (p > 0.05). Adjusting for Care Need Index had almost no effect on the outcomes for the groups.

Conclusions

Frequent users of a national online knowledge base received higher ratings on patient experiences, but figures on health care quality in diabetes showed near to no correlation. The findings indicate that some effects may be attributed to the use of knowledge bases and requires a controlled evaluation.
Literature
1.
Fossum M, Herbst K, Harper L, Castagnetti M, Beckers G, Bagli D, et al. Evidence-based medicine 1: background. J Pediatr Urol. 2019;15(1):78–9.PubMedCrossRef
2.
Gillam S, Siriwardena AN. Evidence-based healthcare and quality improvement. Qual Prim Care. 2014;22(3):125–32.PubMed
3.
Van de Velde S, Heselmans A, Delvaux N, Brandt L, Marco-Ruiz L, Spitaels D, et al. A systematic review of trials evaluating success factors of interventions with computerised clinical decision support. Implement Sci. 2018;13(1):114.PubMedPubMedCentralCrossRef
4.
Cook DA, Sorensen KJ, Wilkinson JM, Berger RA. Barriers and decisions when answering clinical questions at the point of care: a grounded theory study. JAMA Intern Med. 2013;173(21):1962–9.PubMedCrossRef
5.
Djulbegovic B, Guyatt GH. Progress in evidence-based medicine: a quarter century on. Lancet. 2017;390(10092):415–23.PubMedCrossRef
6.
7.
8.
9.
Mondoux S, Shojania KG. Evidence-based medicine: a cornerstone for clinical care but not for quality improvement. J Eval Clin Pract. 2019;25(3):363–8.PubMedCrossRef
10.
Le JV. Implementation of evidence-based knowledge in general practice. Dan Med J. 2017;64(12):B5405.PubMed
11.
Akehurst J, Sattar Z, Gordon I, Ling J. Implementing online evidence-based care pathways: a mixed-methods study across primary and secondary care. BMJ Open. 2018;8(12):e022991.PubMedPubMedCentralCrossRef
12.
Maggio LA, Aakre CA, Del Fiol G, Shellum J, Cook DA. Impact of clinicians’ use of electronic knowledge resources on clinical and learning outcomes: systematic review and meta-analysis. J Med Internet Res. 2019;21(7):e13315.PubMedPubMedCentralCrossRef
13.
Porat T, Delaney B, Kostopoulou O. The impact of a diagnostic decision support system on the consultation: perceptions of GPs and patients. BMC Med Inform Decis Mak. 2017;17(1):79.PubMedPubMedCentralCrossRef
14.
Lobach D, Sanders GD, Bright TJ, Wong A, Dhurjati R, Bristow E, et al. Enabling health care decisionmaking through clinical decision support and knowledge management. Evid Rep Technol Assess. 2012;203:1–784.
15.
Aakre CA, Pencille LJ, Sorensen KJ, Shellum JL, Del Fiol G, Maggio LA, et al. Electronic knowledge resources and point-of-care learning: a scoping review. Acad Med. 2018;2018(11S Association of American Medical Colleges Learn Serve Lead: Proceedings of the 57th Annual Research in Medical Education Sessions):S60–7.CrossRef
16.
17.
18.
Hoque DME, Kumari V, Hoque M, Ruseckaite R, Romero L, Evans SM. Impact of clinical registries on quality of patient care and clinical outcomes: a systematic review. PLoS ONE. 2017;12(9):e0183667.PubMedPubMedCentralCrossRef
19.
Emilsson L, Lindahl B, Köster M, Lambe M, Ludvigsson JF. Review of 103 Swedish healthcare quality registries. J Intern Med. 2015;277(1):94–136.PubMedCrossRef
20.
21.
Al-Janabi S, Patel A, Fatlawi H, Kalajdzic K, Shourbaji IA, editors. Empirical rapid and accurate prediction model for data mining tasks in cloud computing environments. In: 2014 international congress on technology, communication and knowledge (ICTCK); 2014 26–27 Nov. 2014.
22.
Ali SH, editor. Miner for OACCR: Case of medical data analysis in knowledge discovery. In: 2012 6th international conference on sciences of electronics, technologies of information and telecommunications (SETIT); 2012 21–24 March 2012.
23.
Mahdi MA, Al Janabi S, editors. A novel software to improve healthcare base on predictive analytics and mobile services for cloud data centers. Big data and networks technologies; 2020. Cham: Springer.
24.
Al-Janabi S, Mahdi MA. Evaluation prediction techniques to achievement an optimal biomedical analysis. IJGUC. 2019;10(5):512–27.CrossRef
25.
Sundquist K, Malmström M, Johansson S, Sundquist J. Care Need Index, a useful tool for the distribution of primary health care resources. J Epidemiol. 2003;57(5):347–52.
26.
Adolfsson ET, Rosenblad A. Reporting systems, reporting rates and completeness of data reported from primary healthcare to a Swedish quality register-the National Diabetes Register. Int J Med Inform. 2011;80(9):663–8.PubMedCrossRef
27.
28.
Diabetesregistret. Årsrapport 2017. Nationella Diabetesregistret (NDR)–Registercentrum Västra Götaland, Göteborg, Sweden; 2018.
29.
Malmström M. Care need index, social deprivation and health: epidemiological studies in Swedish health care. Malmö; 1999.
30.
Nick TG. Descriptive statistics. In: Ambrosius WT, editor. Topics in biostatistics. Totowa: Humana Press; 2007. p. 33–52.CrossRef
31.
Nachar N. The Mann–Whitney U: a test for assessing whether two independent samples come from the same distribution. Tutor Quant Methods Psychol. 2008;4(1):13–20.CrossRef
32.
Olive DJ. Linear regression. Cham: Springer International Publishing; 2017.CrossRef
33.
Olive DJ, Springer International Publishing AG. Linear Regression; 2018.
34.
Wagner WE, Using IBM. SPSS statistics for research methods and social science statistics. Los Angeles: Sage; 2019.
35.
Marteau T, Hollands G, Kelly M. Emerging behavioral and social science perspectives on population health. Agency for Healthcare Research and Quality and Office of Behavioral and …; 2015.
36.
Aakre CA, Maggio LA, Fiol GD, Cook DA. Barriers and facilitators to clinical information seeking: a systematic review. J Am Med Inform Assoc. 2019;26(10):1129–40.PubMedCrossRef
37.
Koppel R. Is healthcare information technology based on evidence? Yearb Med Inform. 2013;8:7–12.PubMed
38.
Scott PJ, Brown AW, Adedeji T, Wyatt JC, Georgiou A, Eisenstein EL, et al. A review of measurement practice in studies of clinical decision support systems 1998–2017. JAMIA. 2019;26(10):1120–8.PubMed
39.
40.
Cook DA, Enders F, Linderbaum JA, Zwart D, Lloyd FJ. Speed and accuracy of a point of care web-based knowledge resource for clinicians: a controlled crossover trial. Interact J Med Res. 2014;3(1):e7.PubMedPubMedCentralCrossRef
41.
Cook DA, Sorensen KJ, Hersh W, Berger RA, Wilkinson JM. Features of effective medical knowledge resources to support point of care learning: a focus group study. PLoS ONE. 2013;8(11):e80318.PubMedPubMedCentralCrossRef
42.
Metadata
Title
Use of online knowledge base in primary health care and correlation to health care quality: an observational study
Authors
Christian Gerdesköld
Eva Toth-Pal
Inger Wårdh
Gunnar H. Nilsson
Anna Nager
Publication date
01-12-2020
Publisher
BioMed Central
Keyword
Care
Published in
BMC Medical Informatics and Decision Making / Issue 1/2020
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-020-01313-9

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