Nursing Documentation Time During Implementation of an Electronic Medical Record

Transcription

1 14 Nursing Documentation Time During Implementation of an Electronic Medical Record Lisa M. Korst,Alea C. Eusebio-Angeja,Terry Chamorro, Carolyn E. Aydin, and Kimberly D. Gregory Introduction Increased documentation needs during EMR implementation may necessitate increased staffing requirements in an already labor-intensive and demanding environment. A work-sampling study was conducted over a 14-day study period, and 18 of 84 (21%) potential 4-hour observation periods were selected. During each period, a single observer made 120 observations and, on locating a specific nurse, immediately recorded that nurse s activity on a standardized and validated instrument. Categories of nursing activities included documentation, bedside care, bedside supportive care, nonbedside care, and nonpatient care. A total of 2160 observations were made.the total percentage of nursing time spent for documentation was 15.8%, 10.6% on paper and 5.2% on the computer. The percentage of time spent on documentation was independently associated with day versus night shifts (19.2% vs. 12.4%, respectively). Despite charting concurrently on both paper and computer, the amount of time spent on documentation was not excessive, and was consistent with previous studies in which neither electronic nor double charting occurred. Despite the numerous and diverse endeavors to establish electronic medical records (EMR), limited success stories are noted [1] and few of these provide direct assistance to others who are trying to achieve the vision promulgated a decade ago by the Institute of Medicine [2]. Complex technical explanations for this general lack of progress predominate [3 5]; however, the ability of an organization to assist its staff through the behavioral and procedural changes required to rework such a fundamental aspect of normal operations is rarely addressed. Incorporating an EMR into an institutional environment inevitably generates a period of implementation that must be accommodated by staff and budgetary processes of the organization [6,7]. The clinical and financial ramifications of these implementation periods can discourage the infusion of EMR systems into American healthcare organizations [8]. Anderson et al. note the unforeseen costs and 304

2 14. Nursing Documentation Time During Implementation 305 organizational consequences, and even failure of these systems [9] and, although experience with failure of EMR systems is not widely published, unsuccessful implementation efforts do occur [10 12]. A specific organizational concern related to the EMR is that nurses are frequently required to double chart during system implementation. Double charting is a vernacular term for the required entry of the same data elements in both computer and paperbased systems. Education and certification of hundreds of users on a new system and the completion of technical tasks to go live (e.g., achieving appropriate application configuration for the medical center environment, establishing server setup and networking, testing troubleshooting protocols, and meeting security and archival requirements) can generate an extensive period in which users must chart simultaneously on both paper and computer systems. Increased documentation needs can increase staffing requirements and costs in an already labor-intensive and demanding environment [13 18]. This study was done to determine, within the context of all nursing duties, the amount of time spent by nurses for documentation in the patient record during such an EMR implementation period on an intrapartum unit. Methods The study was undertaken at Cedars-Sinai Medical Center, a tertiary care, university-affiliated community hospital in Los Angeles, performing approximately 6700 births per year. The unit has 20 rooms: 12 labordeliveryrecovery rooms and 8 labor rooms. An EMR system to document most of the labor and delivery process was installed in December 1998, and nurses were trained on the system during the first quarter of Nurses were instructed to chart on the computer in addition to maintaining their routine documentation on paper. Elements of computer documentation were a subset of the elements of normal paper documentation. Specifically, computer documentation included a summary of patient history; a flow sheet of nursing annotations about the labor, delivery, and recovery process; vital signs; and delivery summary. Additional paperbased documentation included comprehensive patient history; current problems, medications, and orders; and details of any operative process. The study took place from June 18 through July 1, 1999, and was approved by the Cedars-Sinai Medical Center Institutional Review Board. A work-sampling study design was chosen for its ability to describe the proportion of nursing time spent on documentation activities relative to other duties. The rationale and details of such an approach have been well described by Sittig [19], and the work-sampling methodology has been contrasted with time and motion studies by Finkler et al. [20]. The potential categories of nursing activities were detailed before the study began and an observer, using a sampling mechanism to minimize bias, encountered the

3 306 L.M. Korst et al. nurses in sequence to identify and chronicle their activities. From the percentage of all observations in a specific activity category, the percentage of all nursing time spent in that activity can be inferred. A list of potential nursing activities in the intrapartum unit was modified from previously published studies [21,22], and tested over five 4-hour sessions before the study period. Activities were divided into five major categories: documentation, bedside care, bedside supportive care, nonbedside care, and nonpatient care activities (Table 14.1). Documentation activities included both paper and computer documentation, and providing assistance to other nurses learning to chart on the computer. Bedside care included both direct and indirect patient care; however, bedside supportive care was categorized separately (defined as those activities related to providing physical comfort, emotional support, instruction or information, and advocacy). Nonbedside care activities included intraunit transit, indirect care, reporting, advocacy, and assistance with operative deliveries. Non- TABLE Categories of nursing activity. Category Subcategory Examples Documentation* Paper charting Both bedside and nonbedside documentation of patient care Computer charting Both bedside and nonbedside documentation of patient care Superuser Assisting others with computer charting Bedside care Direct care Positioning fetal heart rate monitor Indirect care Preparing an IV at the bedside Bedside supportive care Advocacy support Speaking to physician regarding a patient s needs Emotional support Assisting the patient with pain management Instructional support Educating the patient regarding breathing techniques Physical support Assisting patient with pushing Nonbedside care Transit Delivering blood specimens to nursing station Indirect care Preparing medication Reports Giving reports during shift change Cesarean room Circulating in the operating room Advocacy Communicating to the family in the waiting room Nonpatient care Meal break Eating or drinking in the nursing lounge Social Time spent in conversation unrelated to patient care Personal Time spent for personal needs unrelated to meal breaks or socializing Not found Unknown location Other Administrative work Off unit Obtaining meals or on a break off the unit * Documentation (both computer and paper-based charting) could occur at the bedside or at a centralized nursing station.

4 14. Nursing Documentation Time During Implementation 307 patient care activities consisted of personal time (including restroom breaks), meal breaks, off-unit activities, and social interaction or conversations about non patient-related activities. Only one of these five categories of activity was assigned per observation. Validation of the correct categorization of nursing activity was assured during the pilot period. At the onset of the study period, the nurse manager informed staff that a study related to implementing the computer program would occur, and that they would be observed over the next 2 weeks. A single observer, a third-year medical student who had previously completed a month of obstetric training within the intrapartum unit, collected the data.the prior clinical rotation familiarized the observer with the unit, the nurses, and the types of nursing activities. Additionally, the observer, now comfortable with the personnel, could move throughout the unit without necessarily interrupting nursing activities. A 4-hour observation period was chosen to minimize observer fatigue. Over a 14-day study period,we randomly selected 9 of 42 (21%) 4-hour potential observation episodes during the day (7 a.m. to 7 p.m.) and 9 additional 4-hour episodes during the night (7 p.m. to 7 a.m.). For each of the 4-hour episodes (N = 18), 120 observations were collected, totaling 2160 total observations. Sample size requirements were determined using the following relationship: n = P(1 - p)/s 2, where n = the total number of observations, P = expected percent of time required by the most important category of the study, and s =standard deviation of the percentage (19).Thus, to establish that the percentage of time that nurses spend charting (conservatively) is 30% ± 2% with a 95% confidence interval (CI), p = 0.3, 2s =2% (s =0.01), so that n = 0.3(1-0.3)/(0.01) 2 = Recognizing that nursing activities are likely to be influenced by the daily census, for each observation period, the observer would note the number of patients and nurses on the unit at the start of the interval and the number of deliveries performed during the interval. The nurses on duty were observed in random order, and the activity category of each nurse recorded at the moment when she or he was found. If a nurse was not found within 5 minutes, her or his activity was labeled Not Found. Patient activity was not observed or recorded. Data were tabulated and analyzed using SAS statistical software, version 6.12 (Cary, NC). Analytical comparisons were made using the Student t test, or nonparametric tests, as appropriate. Probabilities are expressed ± the standard error. Statistical significance was defined as P <.05. Odds ratios comparing stratified data include 95% confidence interval (CI). Results For the 4-hour sampling periods (N = 18), the median number of nurses assigned to patients was 8 (range 7 12). The median patient census was 8 (range 3 14), and the median number of deliveries occurring during each of these periods was 2 (range 1 5). Of the 2160 data points collected, 1080

5 308 L.M. Korst et al. were collected on each shift. The estimated percentage of time spent by nurses on each activity is described in Table 14.2, with 15.79% (95% CI 14.25, 17.33) spent on all documentation: paper charting used 10.55% of nursing time, compared with 5.24% for computer charting. Nurses spent 11.39% of their time charting at the bedside, compared with 4.40% at other unit work areas. The total estimated percentage of time spent in patient-care documentation differed between day and night shifts: 19.17% (95% CI 16.70%, 21.57%) and 12.41% (95% CI 10.44, 14.38) for days and nights, respectively. Because of this difference between day and night shifts, all five categories of nursing activities were compared between the two groups (Table 14.2). In addition to medical record documentation, the day shift spent a greater proportion of time providing direct and indirect bedside care and nonbedside care compared with the night shift. The median total number of deliveries and the total number of nurses varied only slightly per shift: 9 nurses (range 7 12) for days versus 8 nurses (range 8 9) for nights; and 3 deliveries (range 2 5) during a day shift observational period versus 1 delivery (range 1 2) during a night shift. However, the median patient census varied noticeably per shift: 10 (range 6 14) for the day shift versus 4 (range 3 11) for the night shift (P <.01). For this reason, the observations were stratified by shift and each activity category was examined by how busy the unit was during the observation period (Table 14.3). A period with a census of 9 or more patients was defined as a TABLE Estimated percent of time used in nursing activities on an obstetrical unit by shift. Percent estimate ± 95% Confidence Interval Activity Shift standard error (percent time) Documentation* Day ± , Night ± , Total ± , Bedside direct and indirect care Day ± , Night ± , Total ± , Bedside support Day ± , Night 8.98 ± , Total ± , Nonbedside care Day ± , Night ± , Total ± , Noncare Day ± , Night ± , Total ± , * Summary of percent estimates may vary slightly due to rounding; documentation includes computer and paper-based charting. Statistically significant difference.

6 14. Nursing Documentation Time During Implementation 309 TABLE Odds ratios for nursing activities by unit census or busyness of the unit, stratified by shift. Days Activity Percent when busy Percent when not busy Odds ratio 95% CI P value Charting 18.9% (136/720) 19.7% (71/360) Bedside care 22.6% (163/720) 17.8% (64/360) Support 10.3% (74/720) 13.9% (50/360) Nonbedside care* 31.7% (228/720) 25.0% (90/360) Nonpatient care* 16.7% (120/720) 23.6% (85/360) (reversed) Total 100% (720) 100% (360) Nights Activity Percent when busy Percent when not busy Odds ratio 95% CI P value Charting 15.0% (36/240) 11.7% (98/840) Bedside care* 22.1% (53/240) 14.1% (118/840) Support* 14.6% (35/240) 7.4% (62/840) Nonbedside care* 20.0% (48/240) 12.4% (104/840) Nonpatient care* 28.3% (68/240) 54.9% (461/840) (reversed) Total 100% (240) 100% (840) CI = confidence interval, P <.01. * Statistically significant difference. A busy unit was defined as a census of 9 or more laboring patients.

7 310 L.M. Korst et al. busy period in an effort to split the observations into groups falling above or below the approximate median number of nurses available (i.e., the point at which nurses would be assigned the care of more than 1 patient in labor). The amount of time spent on documentation did not appear to be associated with the unit census; however, the proportion of time spent on most other nursing activities was related to the unit census, especially at night. A busy period was associated with an increased proportion of time spent on bedside care, supportive care, and nonbedside care activities. Limitations Although multiple limitations are inherent in the work-sampling method, few tools provide administrators with such a feasible and comprehensive view of their staff activities. Such methods are most comparable to time and motion studies, which can be much more precise because they make fewer assumptions, use contiguous observations, and can detail the duration of any specific task. However, because of the intensity of the observation needed in such studies, the activities of fewer individuals (and their idiosyncrasies) are examined, and the results may not necessarily be generalizable to the remaining staff. Further methodologic concerns are that work-sampling observations will inherently be biased by the sampling periods and that it is difficult to select sampling periods and the order of nurse observation completely randomly. For example, whether a 4-hour sampling period was at the beginning, middle, or end of a shift can very well have been associated with the amount of time spent in documentation. Thus, although a large number of observations were made per shift, the distribution of those sampling periods adds another level of variance to the estimated percentage time spent in that activity. As with any observational methodology, workers can change their work patterns on seeing the observer [19]. We feel this was not likely given (1) the instantaneous nature of the observations and (2) the high comfort level of the nurses with the observer. We used only one observer in this study to maintain the comfort level of the nurses with the observer and, therefore, interobserver variation was not estimated. This is a potential limitation to generalizing our findings; however, they do appear credible when evaluated in the context of similar published work. For example, our estimate of percent of nursing time spent in bedside supportive activities (10.23%) is comparable to estimates by Gagnon et al. (6%) and McNiven et al. (9%) [21,22]. Finally, work-sampling methods do not address the complexity of the nurse patient care relationship, but only measure the amount of time spent in performing observable activities [23]. For example, critical thinking and evaluation processes are often not observable, and multiple tasks can be occurring during any observation. Although we used a validated instrument

8 14. Nursing Documentation Time During Implementation 311 for coding activities, the quality and complexity of that activity was never assessed. Discussion To improve efficiency and quality of patient care, hospitals are increasingly relying on computer technology to improve efficiency and accuracy in documentation [24 27]. To benefit from technologic advances in patient care, the study hospital implemented an EMR to document the progress of a woman in labor. The system continuously monitors uterine contractions and fetal heart rate and allows the user to chart the progress of labor, including interventions, at the bedside computer or at any computer on the unit that is part of the system. Many of the users initially expressed concerns that the new computerized method of charting would be more timeconsuming and would detract from patient care. Our study was done during the transition from paper to computer charting, when the nurses were still charting both by paper and by computer. We found that less time was spent charting by computer than by paper (5.1% vs. 10.5%, respectively) and we estimated that the total amount of time (15.79%) spent documenting on both paper and the computer was comparable to reports based on paper charting alone [21,28]. This study should allay some administrative and user concerns regarding the magnitude of increased nursing resources during the implementation of an EMR. To our knowledge, this is the first study that specifically addresses the double charting situation. The additional time required does not appear to be excessive when compared with estimates of documentation time for systems based on paper alone. A work-sampling study in an obstetric unit estimated 10.2% of nursing time spent in medical record documentation outside of the patient s room [21]. In our study, less than 5% of all nursing documentation took place outside the patient s room. Computer workstations had been deliberately placed at the bedside to encourage nurses to stay with the patients in labor. Another study in a medical surgical unit estimated 11% of total time was spent on nursing documentation on the day shift and 18% on the night shift [28]. We also noted important shift differences, with 19% of time spent on documentation during the day shift and 12% during the night shift. Additionally, the time spent during the night shift performing other nursing activities appeared to be highly associated with the unit census. Concern about the potential for increased staffing requirements can paralyze EMR system implementation. Such operating costs can be difficult to budget. Although administrators may expect that an EMR will eventually save nurses time, evidence suggests that such systems will never allow any actual decrease in labor costs, but only enable nurses to decrease time spent on documentation and shift their time to direct patient care activities [29].

9 312 L.M. Korst et al. Pabst et al. [30] analyzed the effect of computerized documentation on nursing time. They found that switching to a computerized documentation system enabled nurses to spend less time on documentation and more time in direct patient care. Nurses could also update care plans more easily. They were not able, however, to increase patient loads with the saved time, and nurse managers were challenged to find ways to maximize the time saved as a result of the new technology. Given data regarding the importance of supportive personnel on labor outcome [31 36], opportunities to improve quality of care by increasing bedside supportive care could be realized if documentation time is minimized by implementing EMR. The work-sampling methodology used in this study provided time estimates for documenting patient care and also multiple other nursing activities. Although these activities are tangential to the present investigation, they can offer some insight regarding efficiencies that can be gained in activities other than documentation. For example, the high proportion of nursing time used in non patientcare activities (34%) suggests the possibility of using nonprofessional staff members instead of nurses for some activities, or redesigning the roles of existing personnel to include new responsibilities [23,37]. Alternatively, differences in the proportion of time spent in the various nursing activities between day and night shifts may encourage hospital administrators and nurse managers to consider innovative ideas for restructuring nursing time. For example, if less direct patient bedside care is needed at night, yet census status mandates a certain staffing ratio, some administrative duties or enhanced patient or nursing educational opportunities may be redirected to the night shift. Summary In summary, our data suggest that the implementation of an EMR in an intrapartum unit was not associated with excessive time spent on documentation. However, the data also suggest that other opportunities for improving nursing efficiency do exist. Work-sampling methods appear useful for estimating the allocation of nursing time and for suggesting areas in which staff activities can be optimized. References [1] R.A. Garibaldi, Computers and the quality of care A clinician s perspective, N Engl J Med 338(4) (1998) [2] R. Dick and E. Steen, editors, Committee on Improving the Patient Record, Division of Health Care Services, Institute of Medicine, The Computer-Based Patient Record, An Essential Technology for Health Care (National Academy Press, Washington, DC, 1991). [3] R.A. Miller and R.M. Gardner, Recommendations for responsible monitoring and regulation of clinical software systems, J Am Med Inform Assoc 4 (1997)

10 14. Nursing Documentation Time During Implementation 313 [4] E.H. Shortliffe, The evolution of electronic medical records, Acad Med 74(4) (1999) [5] C.J. McDonald, The barriers to electronic medical record systems and how to overcome them, J Am Med Inform Assoc 4 (1997) [6] T.R. Prince and J.A. Sullivan, Financial viability, medical technology, and hospital closures, J Health Care Finance 26(4) (2000) [7] E. Miller and E. Arquiza, Improving computer skills to support hospital restructuring, J Nurs Care Quality 13(5) (1999) [8] J. Ash, Organizational factors that influence information technology diffusion in academic health sciences centers, J Am Med Inform Assoc 4 (1997) [9] J.G. Anderson, C.E. Aydin, and S.J. Jay, Evaluating Health Care Information Systems: Methods and Applications (Sage Publications, Thousand Oaks, CA, 1994) p [10] B.L. Goddard, Termination of a contract to implement an enterprise electronic medical record, J Am Med Inform Assoc 7(6) (2000) [11] M.E. Frisse, Computers and productivity: Is it time for a reality check? Acad Med 73 (1998) [12] T. Chamorro, Computer-based patient record systems, Semin Oncol Nurs 17(1) (2001) [13] M.D. Sovie, Nursing staff in hospitals and nursing homes: Is it adequate? A review and commentary, Nurs Outlook 44(2) (1996) [14] P.M. Watson, M.S. Lower, S.M. Wells, S.J. Farrah, and C. Jarrell, Discovering what nurses do and what it costs, Nursing Management 22(5) (1991) [15] M.J. Fitzpatrick, M.J. McElroy, and S. DeWoody, Building a strong nursing organization in a merged, service line structure, J Nurs Admin 31(1) (2001) [16] E. Ammenwerth and R. Haux, A compendium of information processing functions in nursing: Development and pilot study, Computers in Nursing 18(4) (2000) [17] G.P. Lenehan, ED short staffing: It is time to take a hard look at a growing problem and strategies such as standard nursepatient ratios, J Emerg Nurs 25(2) (1999) [18] A.M. McDaniel, C. Matlin, P.R. Elmer, K. Paul, and G. Monastiere, Computer use in staff development: A national survey, Journal of Nursing Staff Development 14(3) (1998) [19] D.F. Sittig, Work-sampling: A statistical approach to evaluation of the effect of computers on work patterns in healthcare industry, Methods Inf Med 32 (1993) [20] S.A. Finkler, J.R. Knickman, G. Hendrickson, M. Lipkin Jr., and W.G. Thoompson, A comparison of work-sampling and time-and-motion studies in health services research, Health Serv Res 28(5) (1993) [21] A.J. Gagnon and K. Waghorn, Supportive care by maternity nurses: A work sampling study in an intrapartum unit, Birth 23 (1996) 1 6. [22] P. McNiven, E. Hodnett, and L.L. O Brien-Pallas, Supporting women in labor: A work sampling study of the activities of labor and delivery nurses, Birth 19(1) (1992) 3 8. [23] L.D. Urden and J.I. Roode, Work sampling: A decision-making tool for determining resources and work redesign, J Nurs Admin 27 (1997) [24] T. Burkle, R. Kuch, A. Passian, U. Prokosch, and J. Dudeck, The impact of computer implementation on nursing work patterns: Study design and preliminary results, Medinfo 8 (1995), Pt. 2,

11 314 L.M. Korst et al. [25] M. Berg, Medical work and the computer-based patient record: A sociological perspective, Methods Inform Med 37 (1998) [26] K.E. Dennis, P.M. Sweeney, L.P. Macdonald, and N.A. Morse, Point of care technology: Impact on people and paperwork, Nurs Econom 11 (1993) , 248. [27] E. Evans, Using computers as clinical tools to improve patient care, Nephrology News Issues 9 (1995) 30 31, 35. [28] V.V. Upenieks, Work sampling: Assessing nursing efficiency, Nurse Manager 29 (1998) [29] D. Adderly, C. Hyde, and P. Mauseth, The computer age impacts nurses, Computers in Nursing 15(1) (1997) [30] M.K. Pabst, J.C. Scherubel, and A.F. Minnick, The impact of computerized documentation on nurses use of time, Computers in Nursing 14(1) (1996) [31] J. Kennell, M. Klaus, S. McGrath, S. Robertson, and C. Hinkley, Continuous emotional support during labor in a U.S. hospital, JAMA 265 (1991) [32] M. Sleutel and M. Guinn,As good as it gets?: Going online with a clinical information system, Computers in Nursing 17(4) (1999) [33] K.D. Scott, G. Berkowitz, and M. Klaus, A comparison of intermittent and continuous support during labor: A meta-analysis, Am J Obstet Gynecol 180(5) (1999) [34] R. Sosa, J. Kennell, S. Robertson, and J. Urrutia,The effect of a supportive companion on perinatal problems, length of labor, and mother-infant interaction, N Engl J Med 303 (1980) [35] J. Zhang, J.W. Bernasko, E. Leybovich, M. Fahs, and M.C. Hatch, Continuous labor support from lab or attendant for primiparous women: A meta-analysis, Obstet Gynecol 88(4) (1996) Pt. 2, [36] E. Hodnett, Caregiver support for women during childbirth (Cochrane review), in: The Cochrane Library (Update Software (Meta-analysis) 3, Oxford, 1999). [37] L. Linden and K. English, Adjusting the cost-quality equation: Utilizing work sampling and time study data to redesign clinical practice, J Nurs Care Qual 8(3) (1994)