Monitoring Patient Cycle Time: Utilizing EMR Data To Assess Patient Flow and Provider Efficiency. East Arkansas Family Health Center (All 5 Sites)

Transcription

1 Monitoring Patient Cycle Time: Utilizing EMR Data To Assess Patient Flow and Provider Efficiency East Arkansas Family Health Center (All 5 Sites) Introduction Tem Woldeyesus, MD Candidate 2016, UCSF School of Medicine- University of California, San Francisco 2013 GE- NMF Primary Care Leadership Fellow The East Arkansas Family Health Center (EAFHC) is a federally qualified patient centered medical home that provides high quality primary care, dental care, and support services to the underserved populations in Crittenden, Poinsett, Mississippi, and Phillips County. EAFHC, located in West Memphis, AR, is the parent site to four other primary care medical offices located in other counties (note: some of the smaller offices may not be qualified medical homes). During my first week at EAFHC, I was impressed with the integration of electronic medical records (EMR) into their daily clinic operations. They seemed ahead of the curve with a majority of the staff well trained to use EMR. After attending their monthly Continuous Quality Improvement (CQI) meetings, I observed that the EMR system was not being utilized to its fullest capacity in the evaluations of clinical operations, and variables tracked in the EMR could expand the evaluation of provider performance. I also observed that patient cycle times were not being evaluated monthly, despite patient satisfaction surveys suggesting cycle times were prolonged. Very interested in CQI and the role of technology in CQI, I wanted to incorporate the data that can be generated by EMR into a developed model that can evaluate provider efficiency through monitoring patient cycle time monthly. 1

2 Background With the implementation of the Affordable Care Act there will be an additional 32 million people insured, with preventative and primary care coverage (Melinda Abrams, 2011). This influx of people into the system calls for community health centers (CHCs) to expand their capacity as primary care providers, as the demand for services increases with increasing consumers. In 2015, CHCs, with the federal financial support, will nearly double their capacity for high quality primary care and continue to raise the bar for comprehensive primary and preventative care (Kaiser Family Foundation, 2013). CHCs must operate at a high efficiency in order to care for such a high volume of patients. In addition to the pressure to match the rising patient demand, CHCs will still have the burden of caring for the complex needs of current and incoming chronic disease patients. Chronic disease management and the complex needs, both medical and social, can significantly extend visit time. These circumstances require CHCs to increase the importance of provider and clinic efficiency, while maintaining excellent patient satisfaction, in order to successfully withstand the influx. Cycle time is a significant indicator of clinical efficiency and capacity, and is commonly used to assess clinical operations/workflow. The office visit cycle time is the duration of time (in minutes) a patient spends at an office visit (Institute for Healthcare Improvement, 2011). Studies have linked the length of patient cycle time to patient satisfaction, clearly demonstrating the importance of the measure (Nicholas M Potisek, 2007). Monitoring this measure can be a very useful tool in identifying bottlenecks and staff opportunities within the clinic workflow. Seizing these opportunities and reducing the cycle time can increase patient satisfaction, as well as staff 2

3 satisfaction. Seizing these opportunities can also have positive implications on revenue and overall productivity. Currently for EAFHC, patient cycle times are not frequently monitored on a monthly basis. I propose developing a model for analyzing patient cycle time, using data tracked by EMR, to evaluate provider and clinic efficiency. Figure 1- Diagram shows the steps taken to extract and interpret the data generated from the EMR BridgeIT tool. Methodology Generate EMR Data EAFHC uses eclinical Works as their EMR platform. eclinical Works records many variables that make up each patient encounter, including appointment time, patient office arrival and departure time, provider for the visit, etc. This platform has a data management and solutions program, BridgeIT, which acts as an export tool for larger data set extraction from their patient database. After reading the BridgeIT manual, I asked to access the patient database. With permission from EAFHC IT, I logged onto the BridgeIT server and extracted patient encounters, with all published variables, between 12/1/2012 and 07/1/2013. The requested date range 3

4 generated 44,010 patient encounters and their corresponding variables. The program also generated false patient encounters that needed to be filtered out. The data was published in excel format. Filter Incompatible Data A majority of the encounters generated by the extraction tool were false encounters or had poorly registered variables. The false patient encounters included no show patients and cancelled appointments, all of which were removed from the excel sheet. There was also a portion of the encounters that had variables that were poorly registered and recorded inaccurate arrival and departure times. I filtered these encounters by examining each encounter that had missing provider labels, departure times before arrival times, and unlikely visit duration (duration time below 8 min.). After both of the above filters were completed, the amount of total compatible data points was 4,497. Data Organization and Analysis The remaining 4,497 patient encounters were organized by provider, date, and site variables. Cycle times were then measured for all patient encounters by calculating the duration between patient departure time and arrival time under each EAFHC provider. I applied an excel function to all patient encounters under each provider which categorized each counter into a duration time grouping. There were four corresponding groups: 0-30mins, 30-60mins, 60-90mins, min, and above 120mins. A second cycle time calculation (labeled duration time after appointment encounter grouping) was made for each encounter, except the appointment time was exchanged with arrival time variable. This analysis was completed for each provider and then reorganized for each clinic site. Please note that the Lepanto clinic was not included in the evaluation due to 4

5 cross staffing from other sites. This made it extremely difficult to separate encounters that occurred at Lepanto from encounters that occurred at other sites. Filter Tardy Encounters and Repeat Analysis A separate data set was made that consisted of on time patient encounters. Patient encounters that had arrival times after their set appointment time, were deemed tardy and filtered out of the data set. Then, analysis was redone on the on time data set (following same protocol as the Data Organization and Analysis above). Plot and Represent Data The data generated and analyzed was presented in bar and pie graph formats and tables. Cycle time average and encounter grouping, Cycle time (non-tardy vs. all encounters), Duration time after appointment encounter grouping, Cycle time encounter percentages across all sites, and EAFHC Cycle times compared against other CHCs were relationships presented in table and graph format. Results Cycle times were calculated for each provider at four of the five EAFHC sites, which included encounters from 12/1/2012 and 07/1/2013 (Lepanto clinic site was excluded). The average cycle time of all encounters across four of the five EAFHC sites was 99.24mins (Figure 1). The largest cycle time average was mins by provider 2, while the lowest cycle time average was 64mins by provider 4 and 8. The encounters were also stratified into total time groups (ex. 0-30min, 30-60min, etc.). The amount of outstanding encounters, encounters lasting longer than 2hrs across four of the five EAFHC sites was 1295 encounters. Provider 2 had the most outstanding encounters, with a total of 260 encounters. Provider 8 had the least outstanding 5

6 Cycle Time Averages & Encounter Grouping Figure 2- Cycle time is a significant indicator of clinical efficiency and capacity, and is commonly used to assess clinical operations/workflow. The office visit cycle time is the duration of time (in mins) a patient spends at office visit. The cycle time was calculated from marked arrival and departure times in EMR by front desk staff. The cycle time averages are displayed along with the amount of encounters that in total time are between each time interval. These calculations were also made for each site, in addition to each provider. Please note that the Lepanto site was not evaluated due to providers that are seeing patients half time at multiple sites. encounters with 12 encounters. The cycle time averages and stratification of encounters was also completed for each EAFHC clinic site. The EAFHC parent site, in West Memphis, had the highest cycle time average (113.76mins) and the most outstanding encounters (1045 encounters lasting longer than 2hrs) across the four sites evaluated. Helena Family Health Center had the lowest cycle time (64.49mins) and least outstanding encounters (22 encounters). However, given their low number of total encounters after the data set was filtered, it is difficult to rely on their data set. The Trumann Family Health center also had a low cycle time average (65.61mins) and 6

7 amount of outstanding encounters (22 encounters), and had two of the fastest providers staffing the location. A consistent staff comment was that the encounters that are tardy have longer cycle times, thus increasing the site averages. For further investigation, On time encounters were compared with all of the encounters, in terms of cycle time and total time duration groups (Figure 3). The n=2,119 n=4,997 Cycle amount Time of (Non-Tardy outstanding Vs. encounters All Encounters) in the on *includes time group all providers* decreased, but there were no significant Figure 3- To normalize for patients that are tardy for appointments, all encounters that arrived after their assigned changes appointment the ratio of time outstanding were removed, encounters and the to total analysis encounters was reran. among In terms each comparison of percentages group. for each duration interval, there was no change after normalizing for patient tardiness. There were also no significant On time changes group in had cycle 685 time outstanding averages encounters, between on which time encounters was 32% of and all all On encounters. time encounters. 7

8 Duration The Time group After with Appointment all the encounters Encounter had 1295 Grouping outstanding encounter, which was 26% of all of the Figure 4-To normalize against patients that arrive earlier than their appointment times, I looked at the duration encounters. patients stayed In fact, from encounters the start that of their made assigned it to their appointment appointment to the on departure time had a time higher from chance clinic. of The duration times after appointment time was displayed along with the amount of encounters those in total time are staying between longer each than time 2hrs interval. than patients that are tardy. Patients that arrive before there assigned appointment time could lengthen their cycle time and could increase site cycle time average. To normalize against this potential issue, the cycle time start point was switched to the assigned patient appointment time, which avoided the lengthening of encounter cycle time due to early arrival. In comparison to the cycle time of encounters that had their cycle time start at arrival, there was a slight decrease in the cycle time average in the Duration Time After Appointment Encounter group from 99.24mins to 96.23mins. The amount of outstanding encounters also slightly decreased from 1295 outstanding encounters 8

9 to 1256 encounters. There were not any significant changes in the distribution of encounters across all the time duration groups. Comparison of cycle time performances across all sites, specifically the differences in the distribution of encounters across the different time duration groups (Figure 5). EAFHC (West Memphis site) had the highest percentage of outstanding encounters, with 40% of the encounters lasting longer than 2hrs. The Trumann site had the lowest percentage of outstanding encounters, with 4% of their encounters lasting longer than 2hrs. EAFHC and the Healthy Partners site had more than 50% of their encounters longer than 1.5hrs, while the Trumann and Helena site had less than 20% of their encounters above 1.5hrs. The Trumann and Helena sites also had the best 9

10 cycle time performances, with 46% and 52% of their encounters lasting 60mins and below. Overall, there are significant differences in the distribution of encounters in duration groups across all the sites. EAFHC Cycle Times (Comparing against goals set by other primary care organizations) Figure 6- According to Family Practice Management and California Healthcare (in addition to consultants at the primary care association), primary care clinics set a goal of 60min/encounter. I compared the performance of EAFHC and looked at the percentage of encounters that are at or below 60 mins (encounters that are at goal) and the percentage of encounters that are above 60 mins (encounters NOT at goal). Please note this included all encounters, whether tardy or on time. According to Family Practice Management and California Healthcare Foundation, primary care clinics strive for a goal of 60min/encounter (Backer, 2002) (California Healthcare Foundation, 2009). At EAFHC (All sites), 79% of their encounters do not meet this goal (Figure 6). There were vast differences in the success of the clinic sites meeting the 60min/encounter goal. The Helena site was the most successful at meeting the goal with 52% of the encounters, 10

11 while the EAFHC (West Memphis site) was least successful with 10% of their encounters meeting the goal. Discussion The evaluation completed in this project correlated well with qualitative observations made prior. The West Memphis site, where I spent most of my time, had a greater patient load and more hectic patient flow. The patient satisfaction surveys and waiting room observations suggested that they would have a long cycle time, the data confirmed, showing the West Memphis site had the longest cycle time average and most outstanding encounters. The three providers with the slowest cycle times were staffed at the West Memphis site, which could be a reason to for their long cycle times. It can also suggest clinic workflow issues. The clinic building is extremely cluttered, making it very difficult to sustain efficient workflow when patient load increases. The EAFHC is planning on moving to a larger facility in the upcoming year, which could improve the workflow and possibly the cycle time. On the contrary, the Trumann site was established to have the most efficient patient flow by patients and staff. My observations also suggested that they had an excellent workflow. The data corroborated my observations. The providers staffing the site had the fastest cycle time averages and the lowest outstanding encounters. Additional investigation into the patient flow at Trumann Family Health Center needs to be completed to gain a greater understanding for their cycle time differences. The staff was concerned that tardy encounters had longer cycle times, which increased site cycle time averages. This was not confirmed by the data. The cycle time averages between On time encounters against all encounters showed no significant differences. In fact, patients arriving on time had a higher ratio of outstanding encounters. This eliminates tardy encounters as 11

12 a concern for long cycle times. However, it poses a question as to why patients that are on time are not substantially faster in cycle times in comparison to all encounters. Some patients arrive markedly earlier than their appointment time, which would then increase cycle time, since it is defined by when patients set foot into clinic. After changing the starting point for the cycle time to the appointment start time, there were slight decreases in the cycle time and amount of outstanding encounters. The patients that arrive early for their appointments do increase the cycle time, but not enough to be a major component in EAFHC long cycle times. Nonetheless, it could be important for front desk staff to take note of patients arriving early. Early arrival of patients will reduce the accuracy of the cycle time variable, thus taking note of early arrival of patients can increase the accuracy of the evaluation. According to Family Practice Management and California Healthcare Foundation, a common cycle time goal of 60min/encounter is set by many CHCs. EAFHC was not successful in reaching this goal. There were vast differences among the different clinic sites, with some clinic sites significantly more successful than others. If protocols for clinic operations are followed closely, then these inconstancies shouldn't exist. Interventions are needed to provide consistency to the service provided. The data generated by the EMR via BridgeIT can serve as a great tool to evaluate clinical operations. The EMR tracks a variety of variables that could assess clinical operations. Further investigation into these variables can provide additional indicators of clinical operations, and lead to quality improvement initiatives in areas with low performance. EAFHC implementation of EMR is excellent progression, but increased effort must be made in understanding the full capability of the system and its integration in evaluation of clinical operations. 12

13 Recommendations The EMR check-in and check-out times, recorded by the front desk administration, need to be enforced. Since the EMR data extrapolated from the system has never been used prior, my confidence in correctly documented start and end times is not high. If EMR variables are used in the future as productivity indicators, an effort must be made to carefully record the data. Cycle times represent the multiple providers and staff that manage the patient during their visit. The evaluations made in my project can hold more fruitful if the staff involved in each encounter are recorded. I would suggest establishing and documenting provider-nurse pairings. This would narrow the focus of the evaluation, and narrow the solutions/improvement if the evaluations are not favorable. Currently, EAFHC does not provide their staff with quantitative markers of their performance. This can result in staff being unclear on the quality of their performance and the direction they should take to improve it. In addition, confrontation, after observations indicating poor performance, can be perceived as personal attacks without measurements supporting the initial claim. Establishing performance dashboards to review with employees monthly can alleviate this issue. Monthly performance dashboards can be reviewed with staff by supervisors, and areas for improvement can be identified and action plans can be set forth. These action plans must outline measurable improvements and must be time-specific (due dates). Previously mentioned, Provider-nurse teams can be evaluated, and together they can brainstorm solutions to their obstacles and bottlenecks that prevent high productivity. To isolate the bottlenecks that are lengthening the cycle time, flow-mapping encounters would incredibly beneficial. Flow mapping involves recording the duration between each patient 13

14 activity during an office visit. For example, recording the time from when a patient sits in the waiting room and gets called by the nurse for vitals (see sample in appendix). This exercise isolates the procedures/events that lengthen patient flow and provides perspective into where the intervention should be implemented. The patient flow must be examined for opportunities that can expedite a patient visit without sacrificing the quality of care. Works Cited Backer, L. A. (2002, June). Strategies for Better Patient Flow and Cycle Time. Family Practice Management. California Healthcare Foundation. (2009). Workflow Design: A Model for California Clinics. Retrieved from RedesignOPCCModelCalifClinics.pdf Institute for Healthcare Improvement. (2011). Improving Primary Care Access. Retrieved from Office Visit Cycle Time: Melinda Abrams, R. N. (2011). Realizing Health Reform s Potential How the Affordable Care Act Will Strengthen Primary Care and Benefit Patients, Providers, and Payers. Retrieved from The Commonwealth Fund: an/1466_abrams_how_aca_will_strengthen_primary_care_reform_brief_v3.pdf Nicholas M Potisek, R. M. (2007, Jan). Use of patient flow analysis to improve patient visit efficiency by decreasing wait time in a primary care-based disease management programs for anticoagulation and chronic pain: a quality improvement study. BMC Health Services Research. Appendix 14

15 Figure 1- Diagram shows the steps taken to extract and interpret the data generated from the EMR BridgeIT tool. 15

16 Duration Time After Appointment Encounter Grouping Figure 4-To normalize against patients that arrive earlier than their appointment times, I looked at the duration patients stayed from the start of their assigned appointment to the departure time from clinic. The duration times after appointment time was displayed along with the amount of encounters those in 16 total time are between each time interval.

17 n=2,119 n=4,997 Cycle Time (Non-Tardy Vs. All Encounters) *includes all providers* Figure 3- To normalize for patients that are tardy for appointments, all encounters that arrived after their assigned appointment time were removed, and the analysis was reran. In terms of percentages for 17 each duration interval, there was no change after normalizing for patient tardiness. There were also no significant changes in cycle time averages between on time encounters and all encounters.

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19 EAFHC Cycle Times (Comparing against goals set by other primary care organizations) Figure 6- According to Family Practice Management and California Healthcare (in addition to consultants at the primary care association), primary care clinics set a goal of 60min/encounter. I compared the performance of EAFHC and looked at the percentage of encounters that are at or below 60 mins (encounters that are at goal) and the percentage of encounters that are above 60 mins (encounters NOT at goal). Please note this included all encounters, whether tardy or on time. 19

20 Recommendations Sample Flow-Mapping (Based on patient events at EAFHC) 20