The United States health-care system is on an unsustainable path, with costs rapidly rising. The U.S. spends more on health care than any other country does1. The ability of providers to lower their costs is an essential part of reducing rising health-care spending. However, hospitals and providers have struggled to manage their costs, in part because of an inability to accurately track and account for costs2-4. This challenge is partly due to difficulty in allocating labor costs to individual patients as well as to flaws in the current cost accounting systems used by many hospitals and other large providers5. Further complicating the ability of providers to lower costs is a national push to implement new electronic medical record (EMR) systems as well as increasing health-care regulation and rising costs of health-care compliance. The recent passage of the Affordable Care Act has further increased this trend of rising costs.
To date, few authors have examined the effects of EMR implementation on care delivery, with many studies suggesting that EMR systems have adverse effects on various metrics following implementation6-10. Previous studies have examined the effects of EMR implementation on primary-care physicians11 and intensive care units6, but these studies have been limited because they examined relative value units or clinical metrics (mortality, length of stay, etc.). These studies did not examine the impact of EMR implementation on cost or on allocation of providers’ time to various activities. Furthermore, the impact of EMR implementation on orthopaedic providers and/or in outpatient settings has not been examined, to our knowledge.
Outside of health care, management accountants have helped firms (including companies such as Coca-Cola, Citigroup, and HSBC) to better understand their cost structure; however, in health care, the implementation of advanced cost systems has lagged12-15. Multiple authors have hypothesized that time-driven activity-based costing (TD-ABC) more accurately captures health-care costs, in part because it does a better job of assigning indirect or overhead costs than do traditional costing methods, which often assign costs solely on the basis of provider volume5,16-18. This difference is especially important, as these indirect labor costs represent a large portion of the total cost of our health-care system2. Thus far, TD-ABC has been used in the field of adult arthroplasty and was felt to be more accurate than existing cost systems were while providing more insight into the effects of provider workflows19,20.
In particular, demand for musculoskeletal care is expected to increase markedly, with demand for procedures such as total knee arthroplasties (TKAs) expected to rise 174% from 2005 to 203021. In our study, we chose to focus on one of the most common surgical problems, knee osteoarthritis, and the patients undergoing TKA. TKA is one of the most commonly performed surgical procedures and thus represents an ideal area to examine the effects of EMR implementation in a typical surgical clinic setting.
Our hypothesis was that the implementation of a new EMR system would increase labor costs. TD-ABC was used to help determine these costs. Thus, our primary outcome measure was a significant difference in the total average labor cost for patient visits to the clinic before and after EMR implementation. Furthermore, we expected a portion of this cost difference to be due to provider time spent documenting patient encounters. We also hypothesized that it would take more than 2 to 3 months for providers to return to baseline productivity after EMR implementation and that no cost efficiencies would be gained 2 years after implementation relative to costs before implementation.
Materials and Methods
Study Design and Location
This study took place in 2 different orthopaedic surgeons’ adult arthroplasty clinics at our academic medical center. Both providers are fellowship-trained adult arthroplasty specialists and had been at the institution for over 5 years. The first attending surgeon saw patients in the clinic 1.5 days per week, while the second attending surgeon saw patients 2 days per week. Both surgeons saw patients who had arthritis of the hip or knee or who had already undergone a TKA. For this study, patients were only included if they were being seen for knee pain thought to be from knee arthritis that had not already been treated with TKA or if the patients had undergone a primary TKA that needed routine follow-up. This study also included some patients coming to the clinics for intra-articular injections into native knees as well as for preoperative TKA visits. All patients who had been managed with a revision TKA on the affected side or who had painful knees following TKA were excluded in order to focus on the more standardized and common forms of knee arthritis and primary TKAs. Hip patients were excluded in an effort to increase uniformity of the study population in order to isolate the impact of EMR implementation.
Patients were prospectively timed by hand (with a stopwatch) throughout their entire clinic visit during 1 of 4 data-collection periods, with each group representing a new, unique cohort. Forty-eight patients were prospectively timed prior to the implementation of a new EMR system in early 2013. Thirty-three patients were timed between 2 and 3 months following the health-system-wide implementation of the EMR (Epic Systems). Thirty-one patients were timed 6 months following implementation, and 31 patients were timed 2 years following implementation. Each patient verbally consented to be timed before his or her clinic visit, and this study was approved by our institutional review board. Basic demographic information, including age, whether the patient was new or had been seen previously at our institution, and the reason for the visit, was collected.
Time-Driven Activity-Based Costing System
The TD-ABC portion of the study started with interviewing different clinic personnel to understand the existing clinic workflows. Providers were observed to help us understand their typical workflows. Activities performed by all personnel who needed to see patients in the clinic were included. Personnel included receptionists, certified medical assistants, radiology technicians, physician assistants, fellows, residents, medical students, and attending surgeons. Typically, 1 midlevel assistant (a physician assistant, fellow, resident, or medical student) would see each patient. From these interviews and observations, a process map was created. Some patient factors, such as whether the patient was scheduled to receive a corticosteroid injection, affected the workflow. However, uncommon (one-off events) and unpredictable deviations from this workflow were not included. The final version of this process map can be seen in Figure 1, with a description of various provider activities and the levels of disaggregation shown in Table I.
TABLE I -
TKA Clinic Activity Descriptions by Medical Professional
||Includes all time that the patient spends speaking with the receptionist before he/she is seen by any other provider. Also includes time that the receptionist is pulling up anything in the patient’s record or documenting anything
||Certified medical assistant
||Includes time spent directing patients to their examination room and talking with him/her before the physician assistant/fellow/resident/medical student sees the patient. Also includes any time reviewing past medical history but not radiographs
|Determining radiograph quality
||Physician assistant/fellow/resident/medical student/certified medical assistant
||Includes any time spent reviewing previous radiographs to determine whether new films are needed. Does not include time spent reviewing old records and radiographs simply before seeing the patient
||Includes all time that the radiology technician spends with the patient making radiographs and putting them into the record system
|Reviewing records and seeing patient
||Physician assistant/fellow/resident/medical student
||Includes all time reviewing previous patient records, reviewing previous or new radiographs, talking with the patient, and performing a physical examination
||Certified medical assistant
||Includes all time spent drawing up an injection, setting the room up for the injection, and retrieving all of the necessary equipment
||Physician assistant/fellow/resident/attending surgeon
||Includes all time performing the injection, including putting on gloves, sterilizing the site, and placing the dressing on afterward
|Discussing plan of care
||Includes all time that the attending surgeon spends with the patient but does not include any time spent performing an injection. Also includes time spent talking with the physician assistant/fellow/resident/medical student about the patient and/or reviewing previous records or radiographs as well as time spent documenting the encounter (usually limited to charge capture and co-signing notes)
||Physician assistant/fellow/resident/medical student/attending surgeon
||Includes all time spent documenting the encounter as well as ordering any necessary laboratory tests or treatments
|Scheduling tests and follow-up visit
||Includes all time spent with the patient after the medical portion of the visit has concluded
Next, cost rates were calculated for each staff member who performed activities that were captured in the process map. Data were collected from our institution’s existing cost system that reflected the cost of staffers’ salaries and benefits. Then, each provider estimated the amount of time (clinic time, research time, administrative duties, etc.) that he or she spent on different activities in a given week. Time available for patient care was calculated by subtracting the number of hours spent on breaks, in meetings, or doing research from the total hours available. When possible, averages of these estimates from several of the same type of employee were used to increase accuracy. Only the proportion of salaries and benefits related to the time spent on clinical care was allocated. For example, if a provider spent 3 days per week on clinical activities, only three-fifths of his or her salary and benefits was allocated to patient care, representing the compensation for the days that he or she was clinically active.
The total visit cost for different types of patients was then calculated using time equations. Time equations multiply the average time spent on each activity by the cost rate of staff members performing the activity and sum them over all the activities in the process map that the patient undergoes. Table II tabulates these average time and cost data and allows for comparison of patients who had different conditions or factors (such as new patient versus return patient status) and comparison of patients who saw different providers (such as one attending surgeon versus another). These data showed which activities and which providers in the clinic were most expensive or consumed the most time. As the process map in Figure 1 shows, not all patients needed radiography or an injection, so the sample size for these activities is smaller. Because only 1 person was available to time all activities and there was some overlap in time between some of the activities (e.g., completing documentation after the patient’s visit and the patient checking out), Table II shows that not all activities were timed for each patient. Time estimates were recorded by volunteers and included the time that providers spent in a work room documenting encounters after they had left the examination room with the patient.
TABLE II -
TD-ABC Timing and Cost Data at 2 to 3 Months
| Checking in (receptionist)
| Taking patient to radiology department (certified medical assistant)
| Making radiograph (radiology technician)
| Evaluating patient (certified medical assistant)
| Reviewing records/seeing patient (midlevel assistant)
| Preparing injection (certified medical assistant)
| Performing injection (attending surgeon or assistant)
| Plan of care (attending surgeon)
| Documentation after visit (midlevel assistant)
| Checking out (receptionist)
|Total receptionist time
|Total certified medical assistant time
|Total radiology technician time*
|Total physician assistant time†
|Total fellow time†
|Total resident time†
|Total medical student time†
|Total attending surgeon time (all patients)
|Total attending surgeon time (for patients seen by the attending surgeon with no assistant)
|Total average cost‡
*Only times for patients needing radiographs are included.†Only patients seen when the physician assistant, fellow, resident, or medical student was the attending surgeon’s primary assistant are included.‡Cost ranged from $10.51 to $87.96.
Costs and time related to various activities were reported at each of the 4 time intervals with descriptive statistics. Significance at the 5% level was assessed with use of analysis of variance (ANOVA). A multivariate regression was performed to isolate the effects of different variables on both total provider time and total labor cost as well the effect of different providers. Of note, we did attempt to compare TD-ABC with our institution’s existing cost system. However, given that the existing system did not allocate costs to individual patient encounters, TD-ABC clearly appeared to be superior.
Total labor costs per patient significantly increased at 2 months post-EMR implementation (p = 0.05) (Table III). However, by 6 months and 2 years, there was no significant difference in provider labor costs (p = 0.68) (Table III and Fig. 2). In contrast, the total time spent by all providers increased following EMR implementation, even though there was no significant difference in the total cost between the 2-month, 6-month, and 2-year time points (p = 0.18) (Table I and Fig. 3).
TABLE III -
Average Total Costs and Time Spent Per Patient at Different Time Points
|Clinic staff time (min)
|Clinic staff cost ($)
|Certified medical assistant time (min)
|Certified medical assistant cost ($)
|Midlevel assistant time (min)
|Midlevel assistant cost ($)
|Documentation time (min)
|Documentation cost ($)
|Attending surgeon time (min)
|Attending surgeon cost ($)
Attending surgeon A saw a decrease in average clinic volume, from 84.5 to 60 patients per week (p < 0.01), following implementation. This decrease in patient volume persisted at all time points. In contrast, attending surgeon B saw an average of 70 patients per week prior to EMR implementation but at 6 months had returned to a similar weekly patient volume (p < 0.01), which persisted at 2 years. The patient mix was similar at all time points (Table IV).
TABLE IV -
Patient Characteristics at Different Time Points*
*Data are given as the number of patients, with or without the percentage in parentheses.
Drivers of patient cost increases from the initial time point to the 2-month interval included a trend toward increased attending surgeon time spent per patient as well as increased certified medical assistant time spent assessing patients (p < 0.001) (Table III). Additionally, assistant providers spent more than twice as long documenting encounters at 2 months following implementation as they had before implementation (p < 0.001) (Table III); however, there was a trend toward less time reviewing records and conducting the history and physical examination (Tables II and III).
When comparing pre-implementation data with data collected at 6 months and 2 years post-implementation, there were no significant differences in attending surgeon time spent per patient (p < 0.41) and attending surgeon time spent per patient actually increased at 2 years from the 6-month interval. However, providers spent more time documenting encounters at all post-implementation time points, with an increased labor cost. After the initial learning period following EMR implementation, providers spent more time documenting encounters (p < 0.001) but less time interacting with patients (p = 0.013) (Table III). Nonetheless, there was a trend toward decreasing time spent documenting encounters from the 6-month to the 2-year time point (Table II, and Fig. 3), even though providers still spent more time documenting encounters than they had pre-implementation. There was a significant difference in time spent on patient history and physical examination (p < 0.028) (Table II).
Interestingly, providers spent more time documenting patient encounters but less time interacting with patients post-EMR implementation. These values were significant at all time points. Certified medical assistants spent more time with patients than they had prior to implementation, but a decrease from the 2-month assessment was observed. Even at 2 years, certified medical assistants spent almost twice as long with patients as they had pre-implementation (Table II).
As more providers implement EMR systems, there is a need to understand how these systems will impact patient care and costs. TD-ABC represents a promising method for understanding costs and workflows, and it has previously been used in the fields of orthopaedic surgery, neurosurgery, and plastic surgery18-20,22,23. Our goal was to use TD-ABC to better understand the cost implications and workflow adjustments of implementing an EMR system and how this effect changed over time, quantifying the learning curve after implementation. To our knowledge, this is the first study evaluating the outcomes of EMR implementation in the field of orthopaedic surgery or using TD-ABC to evaluate EMR implementation in any field.
We found that total labor costs increased at 2 months post-implementation but that there was no difference in cost at 6 months or 2 years. We also found that providers spent twice as long documenting encounters at all times after EMR implementation. This is consistent with the findings of Keshavjee et al., who noted that physician time spent charting patient encounters increased 50% at 6 months after EMR implementation but returned to original levels 18 months after implementation24. We found less time spent reviewing patient records and documenting encounters than did Sinsky et al., although their study was based on time estimates across multiple physician specialties25. Further, our work did not include any of the upfront costs associated with EMR implementation, which are substantial. Fleming et al. found the cost of EMR implementation for a group of primary-care physicians to be more than $46,000 per physician in the first year of a new EMR26, but these costs likely vary by provider organization. If our institution had a similar implementation cost, this would translate to a cost of approximately $12.20 per patient for EMR implementation.
Anecdotally, many providers found the new EMR system to be more cumbersome (more windows to click through, etc.) for data entry than the prior system had been. This may explain a portion of the increased length of clinic visits and provider documentation time post-implementation. Similarly, Chiang et al. found that EMR implementation increased provider documentation time in outpatient ophthalmology clinics by 6.8 minutes per patient27. Interestingly, Chiang et al. also showed a 12% decrease in clinic volumes over the first 3 months post-implementation, although volumes returned to pre-implementation levels by 2 years. In contrast, our data showed that 1 provider did not return to prior patient volumes while the second provider did so by 6 months post-implementation. We believe that this difference was driven primarily by the large difference in number of clinic patients seen per day before implementation (56 versus 35), and these higher clinic volumes were not sustainable post-implementation. Also, certified medical assistants spent more time per patient post-implementation. Incidentally, many providers felt that there were increased time-consuming documentation requirements (such as recording allergies, height, weight, and blood pressure) under the new EMR system.
Our study demonstrated that, post-implementation, providers spent less time reviewing patient records prior to the visit and less time conducting the history and physical examination (Fig. 3), which partially offset the rise in cost from increasing documentation time. Interestingly, as documentation times fell at the 6-month and 2-year intervals, providers spent more time with patients, although not as much as before implementation. This could suggest that providers ultimately were able to spend less time with patients as documentation requirements increased. If so, this could represent a negative trade-off for patient care and leave patients less satisfied, a trend worthy of further study. Previous authors have shown that EMR systems can have a negative impact on both provider communication and the education of residents, while other authors have shown that EMR systems can improve clinical efficiency28-30.
Almost all of the providers in this study performed data entry into the new EMR system by hand (typing) and with the use of templated notes through Epic software. However, 1 of the 2 physician assistants in the study used a dictation system (M*Modal). An individual analysis of this physician assistant’s activities showed that he was no more efficient in his documentation than other providers were. Perhaps implementation of different data-entry systems (use of scribes, other dictation software, or voice-recognition technology) may change this. Finally, the surgeons examined in this study had relatively homogenous clinic populations. A subspecialty with a more heterogeneous patient population, such as hand surgery or oncology, may see greater changes to the clinic workflow post-implementation.
Study Limitations and Conclusion
This study is limited by the inability to measure true costs. Although we used TD-ABC, there is no perfect method for assigning costs to ultimately use as a control, as these underlying true costs are unobservable.
Our study is also limited by the accuracy of the underlying timed data that we gathered. While timed data are thought to be more accurate than alternatives such as surveys of provider time, the accuracy of stopwatch-timed data is limited and it is resource intensive5. Despite these flaws, TD-ABC remains an attractive costing methodology and continues to be highly applicable in health-care contexts where staff costs are high19,20,22,23. Additionally, the labor cost rates per minute are derived from multiple providers’ estimates of how they spend their time, and these estimates are likely imperfect. However, inaccuracies in the labor cost rates would impact all cost estimates in a similar way and likely would not substantially change our findings.
This study is further limited by a lack of data on the quality of care, documentation, and changes in provider reimbursement. Hence, we are unable to study these possible benefits or detriments. Additionally, although our data were taken from the same provider clinics at different time points, it is possible that the patient populations at each time point were different in ways not captured, which could affect our results. For example, of the patients whom our providers saw, 27% and 30% were new during the first 2 time points in the study compared with only 12.9% of the patients seen at the final time point. It is possible that this difference impacted the average time and costs at the 2-year time point but not the proportion of documentation and face-time with the patient.
Finally, there was some turnover of the medical students, residents, and fellows on service during the 2-year period. However, given that the EMR system was implemented across our entire health system, this may not have substantially impacted our results.
In conclusion, using TD-ABC, it appears that provider labor costs can return to previous levels approximately 6 months following EMR implementation. However, in our study, providers continued to spend almost twice as long documenting patient interactions, even at 2 years post-implementation, as they had before implementation. There was also a trend toward decreased time spent reviewing patient records and conducting history and physical examinations as documentation time increased. Labor-cost increases were in part due to increased certified medical assistant time spent with patients at all time points compared with the time certified medical assistants spent pre-implementation. Health-care systems and policymakers should be aware that the length of the implementation period may be approximately 6 months and can lead to substantial disruptions in provider workflows and efficiency and possibly to even less clinician time spent with patients.
Investigation performed at the Department of Orthopaedic Surgery, Duke University, Durham, North Carolina
Disclosure: The authors indicated that they received no internal or external funding for this study. On the Disclosure of Potential Conflicts of Interest forms, which are provided with the online version of the article, one or more of the authors checked “yes” to indicate that the author had a relevant financial relationship in the biomedical arena outside the submitted work (http://links.lww.com/JBJS/E891).
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