Appl Clin Inform 2020; 11(02): 226-234
DOI: 10.1055/s-0040-1705108
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Impact of Patient Census and Admission Mortality on Pediatric Intensive Care Unit Attending Electronic Health Record Activity: A Preliminary Study

Conrad Krawiec
1   Pediatric Critical Care Medicine, Department of Pediatrics, Penn State Milton S. Hershey Medical Center, Penn State Hershey College of Medicine, Penn State Hershey Children's Hospital, Hershey, Pennsylvania, United States
,
Christy Stetter
2   Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, Pennsylvania, United States
,
Lan Kong
2   Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, Pennsylvania, United States
,
Paul Haidet
3   Office for Scholarship in Learning and Education Research, Pennsylvania State University College of Medicine, Hershey, Pennsylvania, United States
4   Department of Medicine, Pennsylvania State University College of Medicine, Hershey, Pennsylvania, United States
5   Department of Humanities, Pennsylvania State University College of Medicine, Hershey, Pennsylvania, United States
› Author Affiliations
Funding The project described was supported by the National Center for Advancing Translational Sciences, National Institutes of Health (NIH), through Grant UL1 TR000127 and TR002014. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The remaining authors have no financial relationships relevant to this article to disclose.
Further Information

Publication History

15 December 2019

27 January 2020

Publication Date:
25 March 2020 (online)

Abstract

Background Physicians may spend a significant amount of time using the electronic health record (EHR), but this is understudied in the pediatric intensive care unit (PICU). The objective of this study is to quantify PICU attending physician EHR usage and determine its association with patient census and mortality scores.

Methods During the year 2016, total EHR, chart review, and documentation times of 7 PICU physicians were collected retrospectively utilizing an EHR-embedded time tracking software package. We examined associations between documentation times and patient census and maximum admission mortality scores. Odds ratios (ORs) are reported per 1-unit increase in patient census and mortality scores.

Results Overall, total daily EHR usage time (median time [hh:mm] [25th, 75th percentile]) was 2:10 (1:31, 3:08). For all hours (8 a.m.–8 a.m.), no strong association was noted between total EHR time, chart review, and documentation times and patient census, Pediatric Index of Mortality 2 (PIM2), or Pediatric Risk of Mortality 3 (PRISM3) scores. For regular hours (8 a.m.–7 p.m.), no strong association was noted between total EHR, chart review, and documentation times and patient census, PIM2, or PRISM3 scores. When patient census was higher, the odds of EHR after-hour usage (7 p.m.–8 a.m.) was higher (OR 1.262 [1.135, 1.403], p < 0.0001), but there were no increased odds with PIM2 (OR 1.090 [0.956, 1.242], p = 0.20) and PRISM3 (OR 1.010 [0.984, 1.036], p = 0.47) scores. A subset of physicians spent less time performing EHR-related tasks when patient census and admission mortality scores were elevated.

Conclusion We performed a novel evaluation of physician EHR workflow in our PICU. Our pediatric critical care physicians spend approximately 2 hours (out of an expected 10-hour shift) each service day using the EHR, but there was no strong or consistent association between EHR usage and patient census or mortality scores. Future larger scale studies are needed to ensure validity of these results.

Protection of Human and Animal Subjects

This study was reviewed by Penn State Health's institutional review board and was determined to be nonhuman research.


 
  • References

  • 1 Rehder KJ, Cheifetz IM, Markovitz BP, Turner DA. ; Pediatric Acute Lung Injury and Sepsis Investigators Network. Survey of in-house coverage by pediatric intensivists: characterization of 24/7 in-hospital pediatric critical care faculty coverage*. Pediatr Crit Care Med 2014; 15 (02) 97-104
  • 2 Verghese A, Brady E, Kapur CC, Horwitz RI. The bedside evaluation: ritual and reason. Ann Intern Med 2011; 155 (08) 550-553
  • 3 Golob Jr JF, Como JJ, Claridge JA. The painful truth: the documentation burden of a trauma surgeon. J Trauma Acute Care Surg 2016; 80 (05) 742-745 , discussion 745–747
  • 4 Groves PS, Manges KA, Scott-Cawiezell J. Handing off safety at the bedside. Clin Nurs Res 2016; 25 (05) 473-493
  • 5 Carayon P, Wetterneck TB, Alyousef B. , et al. Impact of electronic health record technology on the work and workflow of physicians in the intensive care unit. Int J Med Inform 2015; 84 (08) 578-594
  • 6 Hefter Y, Madahar P, Eisen LA, Gong MN. A time-motion study of ICU workflow and the impact of strain. Crit Care Med 2016; 44 (08) 1482-1489
  • 7 Artis KA, Dyer E, Mohan V, Gold JA. Accuracy of laboratory data communication on ICU daily rounds using an electronic health record. Crit Care Med 2017; 45 (02) 179-186
  • 8 Zwaan L, Thijs A, Wagner C, Timmermans DRM. Does inappropriate selectivity in information use relate to diagnostic errors and patient harm? The diagnosis of patients with dyspnea. Soc Sci Med 2013; 91 (91) 32-38
  • 9 Han YY, Carcillo JA, Dragotta MA. , et al. Early reversal of pediatric-neonatal septic shock by community physicians is associated with improved outcome. Pediatrics 2003; 112 (04) 793-799
  • 10 Ernst KD. Electronic alerts improve immunization rates in two-month-old premature infants hospitalized in the neonatal intensive care unit. Appl Clin Inform 2017; 8 (01) 206-213
  • 11 Beam KS, Cardoso M, Sweeney M, Binney G, Weingart SN. Examining perceptions of computerized physician order entry in a neonatal intensive care unit. Appl Clin Inform 2017; 8 (02) 337-347
  • 12 Wong A, Wright A, Seger DL, Amato MG, Fiskio JM, Bates D. Comparison of overridden medication-related clinical decision support in the intensive care unit between a commercial system and a legacy system. Appl Clin Inform 2017; 8 (03) 866-879
  • 13 Rehr CA, Wong A, Seger DL, Bates DW. Determining inappropriate medication alerts from “inaccurate warning” overrides in the intensive care unit. Appl Clin Inform 2018; 9 (02) 268-274
  • 14 Menachemi N, Chukmaitov A, Saunders C, Brooks RG. Hospital quality of care: does information technology matter? The relationship between information technology adoption and quality of care. Health Care Manage Rev 2008; 33 (01) 51-59
  • 15 Amarasingham R, Plantinga L, Diener-West M, Gaskin DJ, Powe NR. Clinical information technologies and inpatient outcomes: a multiple hospital study. Arch Intern Med 2009; 169 (02) 108-114
  • 16 Flatow VH, Ibragimova N, Divino CM. , et al. Quality outcomes in the surgical intensive care unit after electronic health record implementation. Appl Clin Inform 2015; 6 (04) 611-618
  • 17 Chen L, Guo U, Illipparambil LC. , et al. Racing against the clock: internal medicine residents' time spent on electronic health records. J Grad Med Educ 2016; 8 (01) 39-44
  • 18 Kannampallil TG, Jones LK, Patel VL, Buchman TG, Franklin A. Comparing the information seeking strategies of residents, nurse practitioners, and physician assistants in critical care settings. J Am Med Inform Assoc 2014; 21 (e2): e249-e256
  • 19 Kannampallil TG, Franklin A, Mishra R, Almoosa KF, Cohen T, Patel VL. Understanding the nature of information seeking behavior in critical care: implications for the design of health information technology. Artif Intell Med 2013; 57 (01) 21-29
  • 20 Kroth PJ, Morioka-Douglas N, Veres S. , et al. Association of electronic health record design and use factors with clinician stress and burnout. JAMA Netw Open 2019; 2 (08) e199609
  • 21 Grinspan ZM, Eldar YC, Gopher D. , et al. Guiding principles for a pediatric neurology ICU (neuroPICU) bedside multimodal monitor: findings from an international working group. Appl Clin Inform 2016; 7 (02) 380-398
  • 22 Mazur LM, Mosaly PR, Moore C, Marks L. Association of the usability of electronic health records with cognitive workload and performance levels among physicians. JAMA Netw Open 2019; 2 (04) e191709-e191709
  • 23 Shanafelt TD, Hasan O, Dyrbye LN. , et al. Changes in burnout and satisfaction with work-life balance in physicians and the general US working population between 2011 and 2014. Mayo Clin Proc 2015; 90 (12) 1600-1613
  • 24 Murphy DR, Giardina TD, Satterly T, Sittig DF, Singh H. An exploration of barriers, facilitators, and suggestions for improving electronic health record inbox-related usability: a qualitative analysis. JAMA Netw Open 2019; 2 (10) e1912638-e1912638
  • 25 Nolan ME, Cartin-Ceba R, Moreno-Franco P, Pickering B, Herasevich V. A multisite survey study of EMR review habits, information needs, and display preferences among medical ICU clinicians evaluating new patients. Appl Clin Inform 2017; 8 (04) 1197-1207
  • 26 Nolan ME, Siwani R, Helmi H, Pickering BW, Moreno-Franco P, Herasevich V. Health IT usability focus section: data use and navigation patterns among medical ICU clinicians during electronic chart review. Appl Clin Inform 2017; 8 (04) 1117-1126
  • 27 King K, Quarles J, Ravi V. , et al. The impact of a location-sensing electronic health record on clinician efficiency and accuracy: a pilot simulation study. Appl Clin Inform 2018; 9 (04) 841-848
  • 28 Gellert GA, Crouch JF, Gibson LA, Conklin GS, Webster SL, Gillean JA. Clinical impact and value of workstation single sign-on. Int J Med Inform 2017; 101: 131-136
  • 29 Bates DW, Kuperman GJ, Wang S. , et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003; 10 (06) 523-530
  • 30 Nambudiri VE, Watson AJ, Buzney EA, Kupper TS, Rubenstein MH, Yang FC. Medical scribes in an academic dermatology practice. JAMA Dermatol 2018; 154 (01) 101-103