Method and system of measuring and quantifying inefficiencies in a healthcare facility6401055Abstract A method and system for measuring and quantifying inefficiencies in a medical procedure such as a radiological procedure. Inefficiencies are measured and quantified by collecting a plurality of characterization measurements, where each characterization measurement corresponds to an individual step in the procedure. Once the measurements are collected, a sum of squares analysis is performed on them to determine the effect of each step on the procedure. The process steps are further analyzed to determine the activities within them that have the highest impact on the time needed to complete the task or tasks that make up the step. These tasks or key drivers are then subjected to a regression analysis to determine their effect on the time to complete the procedure. The key drivers may then be changed to adjust the procedure as desired. Claims What is claimed is: Description BACKGROUND OF THE INVENTION
TABLE 1
STEP
SEQUENCE
NUMBER PROCESS STEP CHARACTERIZATION MEASUREMENT
1 Ordering of the Military hour of day call comes from
referring
radiological physician to radiology department at hospital
or other
procedure 23 facility.
2 Scheduling of Time of scheduled exam.
procedure 25
3 Waiting for Elapsed time between referring physician call
and
examination 27 scheduled exam time.
4 Registration 29 Elapsed time between patient arrival at
registration
desk of facility and patient arrival in
radiology
department.
5 Patient preparation Elapsed time between moment patient arrives
in
and waiting 31 radiology department and the moment patient
called
for examination.
6 Examination period Elapsed time between moment patient is
called for
33 examination and initial image or scanning
completion.
7 Quality control Elapsed time between initial image or
scanning
review 35 completion and the moment the image is sent
to the
review or reading stack.
8 Image display 41 Elapsed time between moment the image is sent
to the
reading stack and the moment it is viewed
(put on a
view box) by a radiologist.
9 Review and Elapsed time from Exam put on view box by
interpretation of radiologist and exam sent for dictation.
image 43
10 Dictation sub step Elapsed time for dictation of report.
45
11 Transcription 47 Elapsed time from moment dictated report
complete to
transcription complete.
12 Printing 51 Elapsed time from transcription complete to
report
sent to printing.
13 Printing to signature Elapsed time from report sent to printing
to report put
box 52 in radiologist's signature box.
14 Signing of the report Elapsed time from report put in
radiologist's signature
53 box to radiologist signs report.
15 Actual distribution Elapsed time from radiologist signs report
to report
55 sent for distribution.
The measurements listed in Table 1 may be made using various known manual and automated statistical collection techniques. By collecting the characterization measurements for each of the plurality of steps in Table 1, a sum of squares in an ANOVA may be used to determine the effect of each step on the examination procedure 10. The result is a model containing the effect each process step has on the overall measurement of the time needed to complete the procedure 10. This time is equivalent to or may be considered to be the report turnaround time ("RTT"). The results of an exemplary ANOVA based on the characterization measurements of Table 1 are shown in Table 2. The ANOVA illustrated was conducted using Minitab.TM. software.
TABLE 2
STEP PERCENTAGE
SEQUENCE SUM OF OF
NUMBER PROCESS STEP SQUARES CONTRIBUTION
1 Ordering of the 14,481,112 6.2%
radiological procedure
23
1_1 Day of the week
1_1(a) Monday 1,978,734 0.8%
1_1(b) Tuesday 1,701,501 0.7%
1_1(c) Wednesday 3,822,467 1.6%
1_1(d) Friday 7,855,616 3.4%
1_1(e) Sunday 272,808 0.1%
3 Waiting for 18,058,259 7.7%
examination 27
4 Registration 29 12,783,330 5.5%
5 Patient preparation and 16,853,469 7.2%
waiting 31
6 Examination period 8,288,351 3.5%
33 d
7 Quality control review 13,766,310 5.9%
35
8 Image display 41 10,401,856 4.5%
9 Review and 17,796,404 7.6%
interpretation of image
43
10 Dictation sub step 45 12,519,046 5.4%
11 Transcription 47 21,929,350 9.4%
12 Printing 51 21,515,497 9.2%
13 Printing to signature 9,113,492 3.9%
box 52
14 Signing of the report 16,903,437 7.2%
53
15 Actual distribution 55 23,580,814 10.1%
Total 233,580,814 563.2%
The data in Table 2 reflects the results of the ANOVA analysis modified to accommodate experiences encountered during the measurement process. First, the second sequence step, scheduling a procedure 25, was eliminated because the time to complete the step was found to be insignificant in relative comparison to the times required to complete other tasks. In addition, an additional sequence step, day of the week, was added to the analysis because it was found that the specific day that the exam was ordered or performed affected completion of the procedure. This occurred because staffing levels often varied by the day of the week. In general, the first part of the week was fully staffed where the latter part of the week or "weekend," as used herein, was staffed at lower levels. While adding or removing steps based on observations is not required, doing so enhances the accuracy of the analysis. Once the data in Table 2 is obtained, each of the process steps may be further analyzed to determine the activities within each that have the highest impact on the time needed to complete the subject task. In other words, the "key drivers" for each step are determined. As an alternative to analyzing each process step, those process steps having the highest percentage of contribution to the sum of squares total may be identified. Preferably, and as shown in Table 2 and FIG. 2, the top five or six measurements are identified. For the example discussed herein, the top six measurements were waiting for an opening 27, review and interpretation of image 43, transcription 47, printing 51, signing of the report 53, and actual distribution 55. Once the key drivers are identified, whether derived for all or only a portion of the process steps in the procedure 10, a multiple regression method is used to determine the effect of the key drivers on the overall measurement, i.e., the RTT. The regression analysis performed is consistent with standard regression methods where the dependent variable (in this case, one of the process steps in the procedure) is examined in light of its independent variables (the activities or sub-steps that make up each process step). The correlation between the dependent variable and its independent variables determines which activity or sub-step has the greatest impact on the overall process. As with the ANOVA analysis, the regression analysis may be performed using commercially available software such as the Minitab software noted above. Once the important independent variables or key indicators are found, they are modified to change the larger process as desired. This is best understood by reference to Table 3, below.
TABLE 3
PROCESS
STEP KEY DRIVER IMPACT ON RTT
Waiting for Problem: Staffing of Technicians Decrease RTT variation
an opening Solution: Overlap technicians shifts & by 30% and decrease
27 match scheduling of examinations to RTT mean by 120
schedule minutes
Review and Problem: Radiologists batching jobs, Decrease RTT variation
interpretation "overnight" and "weekend" effect by 8%
of image 43 Solution: Move to overlapped shifts
Printing 51 Problem: Batch printing as a result of Decrease RTT variation
radiologists batch work routine. by 11%, lessen
Solution: Printing at end of transcription "overnight" &
and transcribing staffing matched to new "weekend" effect
Radiologists staffing patterns
Signature Problem: Batch review and signature of Decrease RTT variation
box 52 reports by 7%
Solution: 1) Extended radiology coverage
to level loads, signature pull demand
2) Process step disappears with voice
recognition and systems that permit
paperless process
Actual Problem: Reports signed at beginning of Decrease RTT
variation
distribution shift, which amplifies "overnight" & by 14% and decrease
55 "weekend" effect RTT mean by 150
Solution: Electronic signature and minutes
distributive printing
In the example shown in Table 3, the first key driver or indicator found was poor staffing that affected the time a patient had to wait for an available examination time, i.e., an opening. For example, a physician might order a procedure on a Tuesday morning but there may not be an opening until the following morning, causing a wait of about twenty-four hours. This wait is caused, in large part, by the single-shift staffing schedules of radiology departments. For example, if a department operates from 8 am to 4 pm, and all openings are booked for that period, an overnight delay is automatically added to the wait period because the next possible opening will occur the following day. By overlapping two shifts, e.g., 6 am to 2 pm and 12 pm to 8 pm the operating hours for the department are extended to 6 am to 8 pm increasing the number of possible openings in a single day and decreasing the likelihood of an overnight delay. Thus, one solution to reducing the time of the waiting for an opening step 27 is to overlap schedules. The next key driver or indicator found through the regression analysis was batch processing. It was found that radiologists tend to wait until numerous images have accumulated before they are reviewed. Generally, an overnight delay occurred because radiologists reviewed images the day after they had been made. The same would occur at times when staffing levels were reduced, such as might occur on Saturdays and Sundays. These delays cause inefficiencies in transcription because transcribers face times of little activity followed by times where numerous dictated reports must be transcribed. By overlapping the schedule of radiologists, a more consistent stream of dictated reports is generated causing a more consistent production of transcriptions. The improvement is enhanced by matching transcription schedules to radiologist staffing. Interestingly, it was found that batch behavior caused inefficiency in the printing and signature box steps 51 and 52. By effecting changes that cause more consistent production of work product, the RTT was reduced. Further, the results indicate that implementing electronic solutions such as voice-recognition and electronic distribution technologies are likely to eliminate the need for steps affected by batch processing. Thus, it is believed that further improvements in RTT may be made in hospitals and healthcare facilities that implement these technologies. As can be seen above, the present invention provides a structured methodology for improving the efficiency of medical procedures and, more specifically, the production and distribution of radiological information. Various features and advantages of the invention are set forth in the following claims.
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