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

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.

Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review

“…Studies were conducted in the United States (n = 31), 13 , 14 , 22 , 27 , 28 , 43 , 52–76 Europe (n = 1), 29 and Asia (n = 3). 77–79 Studies included a mix of ambulatory (n = 22) 14 , 22 , 27–29 , 52–54 , 56 , 61–66 , 69 , 71–75 and hospital (ie, inpatient and emergency) settings (n = 11) 13 , 55 , 57 , 59 , 60 , 67 , 68 , 70 , 76 , 78 , 79 with 2 involving both. 58 , 77 A majority of those studies involved single sites (77.1%) and were affiliated with an academic institution/teaching hospital (80.0%).…”

“… 22 , 55 , 56 , 69 , 73 Clinician specialties were heterogeneous; over half the studies involved single specialties (general [n = 11], 14 , 27 , 52 , 53 , 61 , 62 , 64 , 71–73 , 75 emergency [n = 2], 57 , 79 intensivist [n = 2], 67 , 70 other [(n = 5] 13 , 54 , 56 , 74 , 78 ), while the remaining were multiple subspecialties (n = 13) 9 , 22 , 28 , 43 , 58–60 , 63 , 65 , 66 , 69 , 76 , 77 or unspecified (n = 2). 55 , 68 Across all studies, most involved general medicine (n = 17) 14 , 22 , 27 , 28 , 43 , 52 , 53 , 59–66 , 69 , 71–73 , 75–77 followed by surgical subspecialties (n = 8), 13 , 29 , 58 , 59 , 66 ,…”

“…While purely qualitative studies were excluded, 5 studies employed mixed methods 28 , 52 , 55 , 61 , 62 (see online Supplementary Table ). Study designs varied, including time-and-motion (TM [n = 5] 28 , 61 , 62 , 67 , 68 ), validation of TM (n = 2), 27 , 70 cohort (n = 15), 13 , 27 , 43 , 54 , 55 , 57 , 59 , 60 , 63 , 64 , 66 , 72 , 74 , 75 , 77 experimental/quasi-experimental (n = 8), 14 , 22 , 29 , 53 , 56 , 58 , 78 , 79 and cross-sectional studies (n = 4). 69 , 71 , 73 , 76 Eight studies evaluated an intervention, 14 , 22 , 52 , 53 , 56 , 58 , 75 , 78 including scribes (n = 3), 14 , 52 , 53 documentation redesign (n = 3), 58 ...…”

“…Qualitative methods employed in the mixed-methods studies involved informal interviews, 62 social network analysis, 55 thematic analysis, 62 focus groups, 52 and self-reported diary. 28 Few studies addressed validity or reliability of measurements in their studies (n = 11) 22 , 52 , 53 , 59 , 60 , 63 , 64 , 67 , 69 , 73 , 78 ; 2 examined interobserver reliability, 28 , 68 2 employed TM approaches to validate novel analytical methods to examine workflow 70 and the use of EHR usage logs to estimate workload, 27 2 examined correlations between self-reported and objective EHR usage log times, 22 , 73 and 1 employed video recording timers to validate EHR usage log times. 58 …”

“…Seven overarching effort constructs emerged ( Table 3 ): (a) general workload such as overall EHR usage (n = 4) 53 , 56 , 68 , 69 ; (b) clinical documentation/review (n = 15) 28 , 29 , 55 , 57–61 , 67 , 72 , 75–79 ; (c) excess workload including EHR usage after hours (n = 15) 13 , 22 , 27 , 52–54 , 59 , 63–66 , 69 , 71 , 73 , 74 and remote access (n = 1) 72 ; (d) administrative tasks, such as inbox management (n = 2) 69 , 73 ; (e) cognitively cumbersome work, such as multitasking (n = 3) 61 , 62 , 68 ; (f) fragmentation of EHR workflow (n = 1) 70 ; and (g) patient interaction/in-person visits (n = 7). 14 , 28 , 29 , 43 , 53 , 62 , 68 Several terms were employed referring to EHR usage afterhours including “work after work,” 66 “pajama time,” 66 and “Clinician Logged-In Outside Clinic” (CLOC) time.…”