Comparison of Manual versus Automated Data Collection Method for an Evidence-Based Nursing Practice Study

Summary

Objective: The objective of this study was to investigate and improve the use of automated data collection procedures for nursing research and quality assurance.

Methods: A descriptive, correlational study analyzed 44 orthopedic surgical patients who were part of an evidence-based practice (EBP) project examining post-operative oxygen therapy at a Midwestern hospital. The automation work attempted to replicate a manually-collected data set from the EBP project.

Results: Automation was successful in replicating data collection for study data elements that were available in the clinical data repository. The automation procedures identified 32 “false negative” patients who met the inclusion criteria described in the EBP project but were not selected during the manual data collection. Automating data collection for certain data elements, such as oxygen saturation, proved challenging because of workflow and practice variations and the reliance on disparate sources for data abstraction. Automation also revealed instances of human error including computational and transcription errors as well as incomplete selection of eligible patients.

Conclusion: Automated data collection for analysis of nursing-specific phenomenon is potentially superior to manual data collection methods. Creation of automated reports and analysis may require initial up-front investment with collaboration between clinicians, researchers and information technology specialists who can manage the ambiguities and challenges of research and quality assurance work in healthcare.

Citation: Byrne MD, Jordan TR, Welle T. Comparison of Manual versus Automated Data Collection Method for an Evidence-Based Nursing Practice Study. Appl Clin Inf 2013; 4: 61–74

http://dx.doi.org/10.4338/ACI-2012-09-RA-0037

 

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Keywords

Automation - comparative study - data collection - evidence-based nursing - nursing methodology research

 

 

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Conflicts of Interest

The authors declare that they have no conflicts of interest in the research.

• References

• 1 Ferranti J, Horvath MM, Cozart H, Whitehurst J, Eckstrand J, Pietrobon R. et al. A multifaceted approach to safety: The synergistic detection of adverse drug events in adult inpatients. Journal of Patient Safety 2008; 4 (03) 184-190.
• 2 Inacio MC, Paxton EW, Chen Y, Harris J, Eck E, Barnes S. et al. Leveraging electronic medical records for surveillance of surgical site infection in a total joint replacement population. Infect Control Hosp Epidemiol 2011; 32 (04) 351-359.
• 3 Bakken S, Stone PW, Larson EL. A nursing informatics research agenda for 2008-18: Contextual influences and key components. Nurs Outlook 2008; 56 (05) 206 214.e3.
• 4 McBride AB. Nursing and the informatics revolution. Nurs Outlook 2005; 53 (04) 183 191; discussion 192.
• 5 Lang NM. The promise of simultaneous transformation of practice and research with the use of clinical information systems. Nurs Outlook 2008; 56 (05) 232-236.
• 6 Kaplan B. Evaluating informatics applications some alternative approaches: Theory, social interactionism, and call for methodological pluralism. Int J Med Inform 2001; 64 (01) 39-56.
• 7 Keenan GM, Stocker JR, Geo-Thomas AT, Soparkar NR, Barkauskas VH, Lee JL. The HANDS project: Studying and refining the automated collection of a cross-setting clinical data set. Comput Inform Nurs 2002; 20 (03) 89-100.
• 8 Cho I, Park HA, Chung E. Exploring practice variation in preventive pressure-ulcer care using data from a clinical data repository. Int J Med Inform 201 80 (01) 47-55.
• 9 Purvis S, Brenny-Fitzpatrick M. Innovative use of electronic health record reports by clinical nurse specialists. Clin Nurse Spec. 2010; 24 (06) 289-294.
• 10 St. Cloud Hospital [Internet]. St. Cloud (MN): CentraCare Health System. [cited 2012 Nov 7]. Available from: http://www.centracare.com/hospitals/sch.
• 11 Office Excel [CD-ROM]. Version 12. Redmond (WA): Microsoft Corporation; 2007
• 12 Hyperspace [CD-ROM]. Version 2010 IU2. Verona (WI): Epic Systems Corporation; 2010
• 13 Toad for Data Analysts [CD-ROM]. Version 3.1. Round Rock (TX): Dell Incorporated, Quest Software; 2012
• 14 Crystal Reports [CD-ROM]. Version 12. Walldorf (Germany): SAP AG Business Objects; 2008
• 15 SPSS Base [CD-ROM]. Version 14.0. Armonk (NY): IBM Corporation, SPSS; 2005
• 16 Pope D, Simonsohn U. Round numbers as goals: Evidence from baseball, SAT takers, and the lab. Psychol Sci 2011; 22 (01) 71-79.
• 17 Bhattacharya U, Holden CW, Jacobsen S. Penny wise, dollar foolish: Buy-sell imbalances on and around round numbers. Management Science 2012; 58 (02) 413-431.
• 18 Petrovskaya O, McIntyre M, McDonald C. Dilemmas, tetralemmas, reimagining the electronic health record. ANS Adv Nurs Sci 2009; 32 (03) 241-251.
• 19 Ash JS, Sittig DF, Campbell EM, Guappone KP, Dykstra RH. Some unintended consequences of clinical decision support systems. AMIA Annu Symp Proc 2007: 26-30.
• 20 Bonney W. Is it appropriate, or ethical, to use health data collected for the purpose of direct patient care to develop computerized predictive decision support tools?. Stud Health Technol Inform 2009; 143: 115-121.
• 21 Braaf S, Manias E, Riley R. The role of documents and documentation in communication failure across the perioperative pathway. A literature review. Int J Nurs Stud 2011; 48 (08) 1024-1038.
• 22 Elkin PL, Trusko BE, Koppel R, Speroff T, Mohrer D, Sakji S. et al. Secondary use of clinical data. Stud Health Technol Inform 2010; 155: 14-29.
• 23 Fadlalla AM, Golob J.J, Claridge JA. The surgical intensive care-infection registry: A research registry with daily clinical support capabilities. Am J Med Qual 2009; 24 (01) 29-34.
• 24 Salem H, Roth E, Fornango J. A comparison of automated data collection and manual data collection for toxicology studies. Arch Toxicol Suppl 1983; 6: 361-364.
• 25 Stewart WF, Shah NR, Selna MJ, Paulus RA, Walker JM. Bridging the inferential gap: The electronic health record and clinical evidence. Health Aff (Millwood). 2007; 26 (02) w181-w191.
• 26 Ledbetter CS, Morgan MW. Toward best practice: Leveraging the electronic patient record as a clinical data warehouse. J Healthc Inf Manag 2001; 15 (02) 119-131.
• 27 Cios KJ, Moore GW. Uniqueness of medical data mining. Artif Intell Med 2002; 26 1-2 1-24.
• 28 Ferranti JM, Langman MK, Tanaka D, McCall J, Ahmad A. Bridging the gap: Leveraging business intelligence tools in support of patient safety and financial effectiveness. J Am Med Inform Assoc 2010; 17 (02) 136-143.
• 29 Ash JS, Sittig DF, Dykstra RH, Guappone K, Carpenter JD, Seshadri V. Categorizing the unintended socio-technical consequences of computerized provider order entry. Int J Med Inf 2007; 76: 21-27.
• 30 Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: The nature of patient care information system-related errors. J Am Med Inform Assoc 2004; 11 (02) 104-112.