Exploring Vital Sign Data Quality in Electronic Health Records with Focus on Emergency Care Warning Scores

Summary

Background: Computerized clinical decision support and automation of warnings have been advocated to assist clinicians in detecting patients at risk of physiological instability. To provide reliable support such systems are dependent on high-quality vital sign data. Data quality depends on how, when and why the data is captured and/or documented.

Objectives: This study aims to describe the effects on data quality of vital signs by three different types of documentation practices in five Swedish emergency hospitals, and to assess data fitness for calculating warning and triage scores. The study also provides reference data on triage vital signs in Swedish emergency care.

Methods: We extracted a dataset including vital signs, demographic and administrative data from emergency care visits (n=335027) at five Swedish emergency hospitals during 2013 using either completely paper-based, completely electronic or mixed documentation practices. Descriptive statistics were used to assess fitness for use in emergency care decision support systems aiming to calculate warning and triage scores, and data quality was described in three categories: currency, completeness and correctness. To estimate correctness, two further categories –plausibility and concordance –were used.

Results: The study showed an acceptable correctness of the registered vital signs irrespectively of the type of documentation practice. Completeness was high in sites where registrations were routinely entered into the Electronic Health Record (EHR). The currency was only acceptable in sites with a completely electronic documentation practice.

Conclusion: Although vital signs that were recorded in completely electronic documentation practices showed plausible results regarding correctness, completeness and currency, the study concludes that vital signs documented in Swedish emergency care EHRs cannot generally be considered fit for use for calculation of triage and warning scores. Low completeness and currency were found if the documentation was not completely electronic.

Citation: Skyttberg N, Chen R, Blomqvist H, Koch S. Exploring Vital Sign Data Quality in Electronic Health Records with Focus on Emergency Care Warning Scores. Appl Clin Inform 2017; 8: 880–892 https://doi.org/10.4338/ACI-2017-05-RA-0075

Keywords

Vital signs - data quality - emergency medicine - electronic health records - clinical decision support systems.

• References

• 1 Dellinger RP, Levy MM, Rhodes A, Annane D, Gerlach H, Opal SM, Sevransky JE, Sprung CL, Douglas IS, Jaeschke R, Osborn TM, Nunnally ME, Townsend SR, Reinhart K, Kleinpell RM, Angus DC, Deutschman CS, Machado FR, Rubenfeld GD, Webb S, Beale RJ, Vincent J-L, Moreno R. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock, 2012. Intensive Care Med 2013; 39: 165-228.
• 2 Pérez N, Nissen L, Nielsen RF, Petersen P, Biering K. The predictive validity of RETTS-HEV as an acuity triage tool in the emergency department of a Danish Regional Hospital. Eur J Emerg Med 2016; 23: 33-7.
• 3 National Early Warning Score (NEWS) National Early Warning Score (NEWS). 2012 (July). https://www.rcplondon.ac.uk/projects/outputs/national-early-warning-score-news Last accessed on: June 3, 2017.
• 4 Churpek MM, Adhikari R, Edelson DP. The value of vital sign trends for detecting clinical deterioration on the wards. Resuscitation 2016; 102: 1-5.
• 5 Farrohknia N, Castrén M, Ehrenberg A, Lind L, Oredsson S, Jonsson H, Asplund K, Göransson KE. Emergency department triage scales and their components: a systematic review of the scientific evidence. Scand J Trauma Resusc Emerg Med 2011; 19: 42.
• 6 Skyttberg N, Vicente J, Chen R, Blomqvist H, Koch S. How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments. BMC Med Inform Decis Mak 2016; 16: 61.
• 7 Dean NC, Jones BE, Jones JP, Ferraro JP, Post HB, Aronsky D, Vines CG, Allen TL, Haug PJ. Impact of an Electronic Clinical Decision Support Tool for Emergency Department Patients With Pneumonia. Ann Emerg Med 2015; 66: 511-520.
• 8 Alsolamy S, Al Salamah M, Al Thagafi M, Al-Dorzi HM, Marini AM, Aljerian N, Al-Enezi F, Al-Hunaidi F, Mahmoud AM, Alamry A, Arabi YM. Diagnostic accuracy of a screening electronic alert tool for severe sepsis and septic shock in the emergency department. BMC Med Inform Decis Mak 2014; 14: 105.
• 9 Karr AF, Sanil AP, Banks DL. Data quality: A statistical perspective. National Institute of Statistical Sciences. https://www.niss.org/sites/default/files/technicalreports/tr129.pdf Last accessed: June 3, 2017
• 10 Juran JM, Gryna FM. McGraw-Hill:: Juran’s Quality Control Handbook; 1988
• 11 Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 2013; 20: 144-51.
• 12 SBU –Swedish Council on Health Technology Assessment. Triage Och Flödesprocesser På Akutmottagningen. En Systematisk Litteraturöversikt. Stockholm; 2010. http://www.sbu.se/sv/publikationer/SBU-ut/varderar/triage-och-flodesprocesser-pa-akutmottagningen/ Last accessed on: June 13, 2017
• 13 Jerlvall L, Pehrsson T. eHealth in the county councils 2017. 2014 1. 1-62.
• 14 di Martino P, Leoli F, Cinotti F, Virga A, Gatta L, Kleefield S, Melandri R. Improving vital sign documentation at triage: an emergency department quality improvement project. J Patient Saf 2011; 7: 26-9.
• 15 Miltner RS, Johnson KD, Deierhoi R. Exploring the frequency of blood pressure documentation in emergency departments. J Nurs Scholarsh 2014; 46: 98-105.
• 16 Wiler JL, Welch S, Pines J, Schuur J, Jouriles N, Stone-Griffith S. Emergency department performance measures updates: proceedings of the 2014 emergency department benchmarking alliance consensus summit. Acad Emerg Med 2015; 22: 542-53.
• 17 Sapkota B, Sirelkhatim A, Devlin T, Pitiyanuvath N, Dellinger C, Fesmire F. Reducing Door-To-Needle Times for Treatment of Acute Ischemic Stroke: Quality Improvement Initiative at a High Volume Comprehensive Stroke Center (P4.309). Neurology 2015; 84 (Suppl. 14) P4. 309.
• 18 Boonstra A, Broekhuis M. Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions. BMC Health Serv Res 2010; 10: 231.
• 19 Genes N, Chandra D, Ellis S, Baumlin K. Validating emergency department vital signs using a data quality engine for data warehouse. Open Med Inform J 2013; 7: 34-9.
• 20 Philip K, Richardson R, Cohen M. Staff perceptions of respiratory rate measurement in a general hospital. Br J Nurs 2013; 22: 570-4.
• 21 Broad J, Wells S, Marshall R, Jackson R. Zero end-digit preference in recorded blood pressure and its impact on classification of patients for pharmacologic management in primary care –PREDICT-CVD-6. Br J Gen Pract 2007; 57: 897-903.
• 22 Wager KA, Schaffner MJ, Foulois B, Swanson Kazley A, Parker C, Walo H. Comparison of the quality and timeliness of vital signs data using three different data-entry devices. Comput Inform Nurs 2010; 28: 205-12.
• 23 Smith LB, Banner L, Lozano D, Olney CM, Friedman B. Connected care: reducing errors through automated vital signs data upload. Comput Inform Nurs 2011; 27: 318-23.
• 24 Dykes PC, Benoit A, Chang F, Gallagher J, Li Q, Spurr C, McGrath EJ, Kilroy SM, Prater M. The feasibility of digital pen and paper technology for vital sign data capture in acute care settings. AMIA. Annu Symp proceedings AMIA Symp 2006: 229-33.
• 25 Prgomet M, Cardona-Morrell M, Nicholson M, Lake R, Long J, Westbrook J, Braithwaite J, Hillman K. Vital signs monitoring on general wards: clinical staff perceptions of current practices and the planned introduction of continuous monitoring technology. Int J Qual Heal Care 2016; 28: 515-521.
• 26 Ward MJ, Froehle CM, Hart KW, Lindsell CJ. Operational data integrity during electronic health record implementation in the ED. Am J Emerg Med 2013; 31: 1029-33.