A Model for Dental Workload Measurement - NCBI

A Model for Dental Workload Measurement WARREN A. PARKER, DDS, MPH, DALE L. WILLIAMS, BS, MS, RICHARD V. MAYOTTE, DDS, MHA, JAMES J. JAMES, MD, DRPH, AND A. DAVID MANGELSDORFF, PHD

Abstract: The primary purpose of the study was to develop a model that would provide an efficient and standardized approach to workload reporting in a nonfee (HMO-like) dental care system. The model was also designed to predict the dental personnel resource requirements in the system as the overall dental needs of the population were already known. To accomplish this, a set of 246 task/procedures representing the broad scope of dental practice was developed. For each task/procedure, a Best Time-weighted Estimate (BTE) in terms of average expected man-minutes of work required for accomplishment was developed

from over 35,000 actual time measurements on patient visits to 29 US Army dental clinics located throughout the United States. Because of the nature of the specific task/procedure data, it was necessary to use four different mathematical models to produce statistically optimal BTEs. It was concluded that, cumulatively, the BTEs developed for each task/procedure evaluated could be used as a basis for both the development of a Dental Care Composite Unit workload measure and the determination of overall dental personnel resource requirements in a non-free dental care system. (Am J Public Health 1982; 72:1022-1027.)

Previous studies on the dental care needs of the active duty US Army population have provided meaningful data on the overall state of the dental health of the population. 1-3 Parker, et al,3 reviewed the dental treatment plans from 13,182 soldiers and found that 87.4 per cent were in need of some form of corrective care and 10.1 per cent required preventive services. The raw data from this latter study also enable a quantification of number and mix of specific dental tasks and procedures that would have to be provided in order to meet the measured overall dental health need. If reliable average task/procedure treatment times could be developed and coupled with the specific needs data, then a model could be produced that would enable both the determination of dental personnel resource requirements and a standardized workload reporting system. This paper describes the development of such a model based on a set of specific task/procedure Best Time-weighted Estimates (BTEs). In the general practice of dentistry, the unit-additive system is the most common approach used to account for workload as a productivity measure. Charges for services rendered are ultimately determined by the number of procedures accomplished, and do not take into account either the specific time required to perform a given procedure, or the number and mix of personnel utilized. In essence, the amount of profit becomes a proxy measure for productivity. Examples of the unit-additive system include the California Relative Value Unit System4 and the American Dental Association (ADA) dental procedure code system.5

In a non-fee for service delivery system, a unit-additive system proves a poor measure of productivity because salaries are fixed, and there is no direct dollar benefit accrued from increasing workload. In this type of system, the number of procedures per man-hour unit of time expended is a more practical and valid measure of the efficiency of dental care delivery.6 Rather than simply counting units, procedures are weighted to reflect the actual time required per procedure. To date, several dental workload models have been developed. Lotzhar, et al,78 measured the time required for dentists and expanded duty dental auxiliaries to perform selected dental procedures. Over the baseline period, seven experienced dentists were timed during their performance of more than 55,000 procedures, and a total of 67 chairside procedure mean times were calculated in the first two phases of the study. However, approximately 85 per cent of the procedures were accounted for by only four of the participating dentists. Kilpatrick, et al,9 developed a computer simulation model for dental practice analysis directed toward the private practice setting which was primarily directed toward revenue producing systems. Unfortunately, the data reported did not address the time required to perform specific procedures. Other reported systems have, in general, been deficient in several respects: 1) too few procedures have been measured; 2) the populations studied have been too limited; and 3) the procedure times have not been based on actual time measurements taken in a normal work environment.'0"11 The practice of dentistry in the United States Army compares to a non-fee-for-service health maintenance organization (HMO). As such, a valid task/procedure accounting measure should be based on a set of time-weighted procedures that accurately reflects the overall practice of Army dentistry. Unfortunately, Army dental productivity measures have historically relied on simply counting all task/ procedures performed and this has provided an unsatisfac-

From the Health Care Studies Division, and the Dental Studies Office, Academy of Health Science, US Army, Fort Sam Houston, Texas. Address reprint requests to: Commandant, Academy of Health Sciences, US Army, Attn: HSHA-CHC (Dr. A. David Mangelsdorff), Fort Sam Houston, TX 78234. This paper, submitted to the Journal November 24, 1981, was revised and accepted for publication February 17, 1982.

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AJPH September 1982, Vol. 72, No. 9

DENTAL WORKLOAD MEASUREMENT

tory basis for determining either resource requirements or actual workload. Therefore, the primary goal of this study was the development of a time-weighted procedure accounting system based on actual time measurements which would reflect dental productivity in a non-fee HMO model generally and the United States Army specifically.

Model Development A task/procedure list was needed to reflect the full spectrum of dental services provided. A set of tasks/procedures developed by Marcus'2'3 was utilized as a core list. This was augmented by additions from American Dental Association lists and from suggestions offered by a panel of eight practicing United States Army general dentists and specialists. The initial cumulative list was submitted to six Army dental consultants for review and revision, and a final list consisting of 246 tasks/procedures subdivided into ten treatment categories was developed for use in the study. This list was used to collect data over a two-week period from 29 Army dental clinics located at various installations throughout the United States. The data base consisted of over 35,000 actual task/procedure time measurements on approximately 15,000 individual dental appointments serviced by approximately 300 dental care providers. For each visit, the following data were collected: specific task/procedure performed, total task/procedure time, provider time expended per task/procedure, and dental facility where service provided. If a specific task/procedure (such as tooth extraction) was repeated during a single visit, the number of repetitions and total treatment time required were recorded. A Best Time-weighted Estimate (BTE) representing the average or expected man-minute time requirement per task/ procedure was developed for each task/procedure in accordance with one of the following four criteria: 1) Unit Additive (UA)-for those task/procedures with constant treatment times per unit, irrespective of the number of units provided, the BTE was defined as the mean arithmetic value for all observations on that task/procedure; 2) Unit Sensitive, Integer Additive (USIA)-for those task/procedures in which the single unit time would be relatively high compared to subsequent units which would require lesser but equal incremental times, the BTE would be defined by simple addition of the average initial unit time plus the average incremental time multiplied by n-I where n would equal the number of units performed in a single visit; 3) Unit Sensitive, Non-additive, Single Factor (USSF)-for those task/procedures provided in multiple units and for which the average unit time varied with the number of repetitions, the BTE was taken as the least squares solution of the linear regression model BTE = m Xi + b in which Xi = the number of repetitions for the task/procedure in a given visit, and; 4) Unit Sensitive, Nonadditive, Multiple Factor (USMF)-for those task/procedures provided in multiple units and for which the average unit time varied with the number of repetitions and some other factor(s) such as complexity of each repetition, a multiple linear regression model was used to formulate the BTE. A more detailed description on the derivations of these AJPH September 1982, Vol. 72, No. 9

formulae are presented elsewhere'4 and are available upon request.

Results Of the 35,165 measurements on 246 tasks/procedures, 25,266 (72 per cent) were accounted for by the 25 most frequently occurring tasks/procedures. These 25 are presented in order of descending frequency in Table I along with their actual frequency, per cent of total, Best Time-weighted Estimate (BTE), and the method used to calculate the BTE. The remaining 221 tasks/procedures accounted for only 28 per cent (9,899) of the overall measurements; these are available upon request.'4 Of the 246 tasks/procedures on which measurements were collected, 205 (83 per cent) were found to be unit additive, and the arithmetic mean of all measurements for a specific task/procedure was taken as the BTE. Table 2 lists representative UA tasks/procedures along with the BTEs in provider man-minutes, the standard deviation, and the number of measurements on which the calculations were based. The BTEs for another 23 (9 per cent) of the tasks/procedures were calculated using the Unit Sensitive, Integer Additive approach, and a sampling of the USIA tasks/procedures and the BTEs calculated in this manner are presented in Table 3 along with the number of measurements used in the calculations. Fifteen (6 per cent) of the tasks/procedures were characterized as Unit Sensitive, Non-additive, Single Factor; their BTEs were thus calculated using a simple linear regression model and the specific values vary with the number of units provided in a given visit. Table 4 displays several of the USSF tasks/procedures along with the linear regression equations necessary to compute the BTE and the number of measurements supporting the equations. Table 5 lists the three (2 per cent) tasks/procedures categorized as Unit Sensitive, Non-additive, Multiple Factor, and for which the appropriate BTE was calculated using a multiple linear regression equation. The specific factors used in the equations are provided, along with the number of measurements used to derive the equations.

Discussion The findings represent an alternative model for measuring dental workload, and, when coupled with data on dental needs, a model for determining dental resource requirements. The model offers a 246-item task/procedure time approach for recording specific clinical activities performed. Unfortunately, because of the number of variables involved and the complexity of the formulae, realization of the full potential of this model is dependent upon the availability of an automated data processing system to analyze and develop the clinical data set which generates the overall indicators of productivity. * *Programs can be made available through Combat Developments and Health Care Studies, ATTN: HSHA-CHC, Academy of Health Sciences, Fort Sam Houston, TX 78234. 1 023

PARKER, ET AL. TABLE 1-Twenty-Five Most Frequently Reported Tasks/Procedures: Frequency and Per Cent of Total, BTE, and Method Used to Calculate BTE

Rank Task/Procedure

1. 2. 3. 4. 5. 6. 7. 8.

Intra Oral Examination Prepare Tooth for Filling Material Post Surgical Treatment Place and Finish Amalgam Chairside Oral Hygiene Counseling Place Liner or Base Periapical Radiographs

Scaling 9. Extraction-Erupted Tooth 10. Dental Prophylaxis Bitewing Radiograph Evaluate Treatment by Auxiliaries Write Prescription Place Temporary Filling Place Rubber Dam-Multiple Teeth Medical-Dental History Blood Pressure/Pulse Place and Finish Synthetic Restorative Material 19. Administrative Interruption 20. Lecture 21. Consultation 22. Panograph 23. Periodontal Examination 24. Subgingival Scaling and Root Planing (quadrants) 25. Oral Disease Control Total 11. 12. 13. 14. 15. 16. 17. 18.

Frequency (%)

Method Calculated By*

BTE

3078 2203 1737 1702 1926 1818 1531 1371 1293 1272 1115 815 703 606 515 508 427

(9) (6) (5) (5)

15.3 ** 8.1

(5) (5) (4) (4) (4) (4) (3) (2) (2) (2) (1) (1) (1)

7.8

383 377 367 338 312 285

(1) (1) (1) (1) (1) (1)

UA USMF UA USSF UA USSF USSF UA USSF UA USIA UA UA USIA UA UA UA

18.9 12.9

3.1 4.0 ** 5.3 5.4 3.1

** 9.4 14.2 9.5 9.8

13.8

USMF UA UA UA UA UA

18.4

USSF UA

285 (1) 277 (1)

25,266 (72)

UA = Unit Additive; USIA = Unit Sensitive, Integer Additive USSF = Unit Sensitive, Non-additive, Single Factor; USMF = Unit Sensitive, Non-additive, Multiple Factor. **No specific value given as BTE is unit sensitive and will vary.

*For method calculated:

Empirically, in many areas of dentistry an economy of time can be realized by performing multiples of like procedures in single visits as opposed to performing single procedures on multiple visits. The performance of quadrant amalgam restorations is an obvious example of this. Typically, however, productivity measures do not take this economy into account. The approach described herein, based on actual task/procedure time measurements, provides a quantitative basis for the development of a productivity measure which does incorporate the effect of performing multiple like tasks/procedures in a single visit. The findings from the study indicate that it is possible to precisely measure in minutes the average time required to perform a dental procedure and that it is also possible to accurately identify those tasks/procedures which can be accomplished in multiple units at a time savings per unit. The findings also identify those tasks/procedures which are not unit sensitive and for which there is no economy of time when performed in multiple units at a single visit. With this model, statistically valid dental productivity measures for individual providers and/or entire facilities can be generated. These can be used for comparison purposes regardless of differences in the overall number and mix of tasks/procedures performed. This 1 024

is possible because all procedures are reduced to a common denominator of man-minutes. In a non-fee delivery system, productivity becomes relatively simple to quantify with the application of this model. Productivity indices would be calculated by dividing TABLE 2-Representative Unit Additive Tasks/Procedures with BTEs in Man-Minutes Required for Accomplishment Task/Procedure

Number of Measurements

Take Medical-Dental History Intra-Oral Examination Blood Pressure/Pulse Develop Treatment Plan Scaling Dental Prophylaxis Open Tooth-Extirpate/Pulpectomy Pulpotomy Gingivectomy (quadrant) Place Periodontal Pack Try-in Fixed Bridge POT Surgical Frenectomy

508 3078 427 103 1371 1272 174 58 70 89 32 1737 10

Standard BTE Deviation

5.4 7.2 3.5 12.2 18.9 12.9 16.1 14.9 24.9 8.6 34.5 8.1 15.5

4.6 7.7 4.7 9.3 10.5 9.7 13.2 18.4 17.9 5.3 31.8 11.1 7.3


DENTAL WORKLOAD MEASUREMENT

TABLE 3-Representative Unit Sensitive, Interger Additive Tasks/Procedures with BTEs in Man-Minutes, and Single Unit and Additive Factor* Single Task/Procedure

Unit BTE

Standard Deviation

Incremental Additive Factor

Measurements

Bitewing* Diagnostic Cast Acid Etch Preliminary Partial Denture Impression Preliminary Full Denture Impression Final Full Denture Impression Adjust Immediate Denture (Full)

7.6 11.1 5.0 13.7 13.1 37.4 13.2

5.3 8.9 3.3 12.6 16.9 26.1 9.0

2.8 3.0 4.3 3.3 3.7 28.0 5.7

1110 82 87 107 57 64 139

Number of

*To calculate the BTE for these tasks/procedures for a given visit, the formula Single Unit BET + ((n - 1) x the additive factor) is used. For example, the BTE for a single visit with three bitewings would be 7.6 + [(3 - 1) x 2.8] = 13.2 man-minutes.

the weighted output during a specific period of time by the total available provider man-hours. Unfortunately, the development of the model to this point has not accounted for variations that might be attributable to differences in provider mix; however, this variable could easily be adjusted for with appropriate programming modifications. Indices of productivity could be generated for each level of a dental care delivery system, down to and including the individual provider. Eventually, a range of acceptable norms could be developed after a period of experience with the system, but, in light of the large variances exhibited in the BTEs for many of the tasks/procedures found in this study (Tables 2 and 3), great care would have to be exercised in using those norms for comparison purposes. Thus, by employing a single common denominator, the time-based weighting system described in this model provides a rational basis upon which to measure productivity. In addition, the model also embraces an equitable allocation of human and material resources within a non-fee dental care delivery system. Like a business where the bottom line is profit or loss in dollars resulting from differences in income versus expenses, all production in a non-fee health facility can be described in common units since its value can be expressed in terms of overall treatment time expended. This value can then be compared to the time available to determine the "profit or loss." As the system quantifies the specific tasks/procedures being accomplished in a given facility, the appropriate number and mix of personnel can be assigned as can the amount and types of specific supplies and equipment. By placing the provision of dental care on a task-oriented time basis, each component (clinic) within the delivery system can be continuously evaluated relative to itself and relative to other components. Finally, when coupled with appropriate dental needs data, the model can be used to predict the overall level of dental manpower resources required to provide the number of tasks/procedures needed to attain a given level of dental health in a population. As alluded to above, one troublesome aspect of the present study is the relatively large magnitude of the variances found for many of the task/procedure BTEs. As the study reported herein gathered data from some 300 different AJPH September 1982, Vol. 72, No. 9

providers, large variances were, in fact, expected. Unfortunately, the data base was not coded so as to permit a detailed variance analysis in order to determine to what degree the larger variances could be explained by provider versus facility effect. Visual examination of the data strongly indicates that the provider or provider-mix effect was much more pronounced than was the effect of the clinic in which the care was rendered. This observation could be easily verified in a working system properly programmed to provide not only the necessary data, but also the required analyses. Unfortunately, this reasoning does not explain the large variances found for such tasks/procedures as Blood Pressure/Pulse for which the standard deviation was found to be greater than the mean. This, of course, raises some legitimate questions as to the inherent validity of the measurements upon which the BTEs were based. Although criteria for specific time measurements were carefully and uniformly defined, the data were collected at 29 different facilities by multiple individuals within those facilities, and there is, undoubtedly, some degree of inter- and intra-observer variation in the data. However, these effects cannot be quantified, and without additional data collection efforts, the relative contributions of naturally occurring versus observer variation to the variances found can only be surmised.

TABLE 4-Representative Unit Sensitive, Non-Additive, Single Factor Tasks/Procedures with BTE Linear Regression Equations** and Measurements Task/Procedure

Prep Tooth/Abutments for Cast Restoration (s) Insert Cast Restoration Extraction" Impaction-Completely Bony

BTE = m Xi * + b

10.05 8.60 3.17 8.76

(Xi) (Xi) (Xi) (Xi)

+ + + +

23.33 11.00 10.45 16.50


130 57 1293 159

*Xi = Number of units for a given visit. "Example: To calculate the BTE for four extractions-BTE = 3.17 (4) 10.45 = 23.13 man-minutes.

+

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PARKER, ET AL.

TABLE 5-Unit Sensitive, Non-Additive, Multiple Factor Tasks/Procedures with BTE Multiple Regression Equations** and Supporting Measurements Task/Procedure

BTE = m, X1 + ... + mN XN + b*


1.18 (number of teeth) + 1.79 (number of surfaces-1) + 8.06 1.81 (number of teeth) + 3.94 (number of surfaces-1) + 7.29

2203 1702

4.05 (number of teeth) + 4.93 (number of surfaces-1) + 7.47

383

Prepare Tooth for

Filling Material*"

Place Amalgam and Finish Place Synthetic Restorative Material and Finish

*For this series of equations X subscript represents the set of factors found to give the best time-weighted estimate (BTE) for a given task/procedure. "Example: To calculate the BTE for 5 surfaces on 3 teeth being prepared for filling material-BTE = 4.05 (3) + 4.93 (5-1) + 7.29 = 39.16 man-minutes.

Still, the data reflect what was measured, and if the variances for dental tasks/procedures are inherently large this fact alone would not be used as an argument against the acceptance of a statistical time-weighted productivity measure. Rather, it should alert potential users to the dangers of simply comparing "means" without regard to the statistical effect of large variances on the probability of occurrence of a wide range of acceptable means. This necessitates the utilization of statistically competent personnel not only in system design, but also in the interpretation of the meaning of system output. The application of the procedures herein described to any non-fee dental care system could be accomplished with limited modifications. The essential first step is the definition of a task/procedure list that adequately and comprehensively reflects the full practice of dentistry for a given system. The 246 tasks/procedures defined by this study would be fairly descriptive for the great majority of dental practices. However, the relative number and mix of tasks/procedures would likely be quite variable across different systems because of differences in the demographic make-up of populations served. After settling on a specific task/procedure list, a data collection system keyed to this list, and preferably automated, would have to be established. Necessary data items by patient visit would then be recorded; these, at a minimum, would include patient identifier, specific provider(s) identifier, tasks/procedures performed with treatment times (in minutes), and facility where performed. In order to achieve maximum precision in data-gathering, especially regarding the measurement of treatment times, a limited number of well-trained observers, and a systematic and controlled collection procedure should be employed. This should do much to reduce the unacceptably large variances found in the present study, or, at least, would enable a better interpretation as to their underlying causes. The collection of these data over time would provide a quantitative description of the given dental system in terms of both the number and mix of tasks/procedures performed and the number and mix of provider man-hours utilized. As time weighted task-procedure norms are developed, an objective productivity measure is made available to the dental care delivery system, and a more equitable and rational distribution of resources can be made between 1026

system components. In terms of utilizing the system to predict manpower and other resource requirements, however, a conscious decision must be made as to whether a "demand" or "needs" model is desired. For a demand model future resource expenditures can be predicted based on present usage rates adjusted for demographic trends, as required, and no additional data gathering efforts are required. However, to use a needs model, as is done in the US Army, an estimation of the overall dental care requirements of the population served must be made, and this, depending on the size of the population covered and the specific measurement approach utilized, could be a formidable undertaking.

REFERENCES 1. Hobson RW: Dental examinations of 8,139 Army recruits. Preliminary report. US Armed Forces Med J 1956; 7:648. 2. Cassidy JE, Parker WA, Hutchins DW: Dental care requirements of male Army recruits. Milit Med 1973; 138:25. 3. Parker WA, Schopper AW, Mangelsdorff AD, et al: Determination of the distribution of dental care needs of the active duty army. Public Health Rep 1979; 94:182. 4. California Department of Mental Hygiene: Relative Value Code Book. Sacramento: Department of Mental Hygiene, State of California, 1968. 5. American Dental Association: Code on dental procedures and nomenclature. JADA 1972; 85:789. 6. Mitry DJ, Johnson K, Mitry NW: Specification of the production function for dentistry: measurement and the para-professional input. Inquiry 1976; 13:152. 7. Lotzkar S, Johnson DW, Thompson MB: Experimental program in expanded functions for dental assistants: Phase I-Base Line and Phase 2-Training. JADA 1971; 82:101. 8. Lotzkar S, Johnson DW, Thompson MB: Experimental program in expanded functions for dental assistants: Phase 3Experiment with dental teams. JADA 1971; 82:1067. 9. Kilpatrick JE, Mackenzie RS, Kisko TM: Dental practice analysis using computer simulation. J Dental Ed 1976; 40:745. 10. Oakley RE, Gray JT: Performance Standards for an Army Dental Service. Hospital Management Research Unit, Brooke Army Medical Center, Fort Sam Houston, TX, 1956. 11. Cleveland: Tennessee Productivity Study. Division of Dental Health, Bureau of Health Manpower Education, DHEW. Washington, DC: Govt Printing Office, 1972. 12. Marcus M: Task Analysis in Dentistry: Computer Application.

Final Report, UCLA School of Dentistry, Los Angeles, CA, 1975.


DENTAL WORKLOAD MEASUREMENT 13. Marcus M, Drabek L: Study: VA Dental Manpower Requirements. UCLA School of Dentistry, Los Angeles, CA, 1976. 14. Parker WA, Williams D, Mayotte R: Dental Care Composite Unit Study: Phase II-Development of a Time-Provider Based Dental Procedure Weighting System. Health Care Studies Division Report #78-006, Academy of Health Sciences (AHSDCDHCS), Fort Sam Houston, TX, 1978.

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ACKNOWLEDGMENTS The authors wish to acknowledge the assistance provided by Marvin Marcus, DDS, MPH, Assistant Professor, Public Health and Preventive Dentistry, School of Dentistry, University of California, Los Angeles, during the planning phase of this study. The views of the authors do not purport to reflect the position of the Departments of the Army or Defense.

Call for Papers on Rural Primary Care

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The Sixth Annual Conference on Rural Primary Care will be held March 6-8, 1983 in Kansas City, Missouri. Contributions from researchers, health professionals, and administrators are invited in the form of: * original research and evaluation * program evaluation and problem-oriented case studies * descriptive, analytic, or methodological papers The deadline for submissions is November 30, 1982. Manuscripts should be approximately 20004000 words in length (8-16 double-spaced typewritten pages), exclusive of tabular material. Five copies of the manuscript and illustrative material should be sent to: Ben F. Banaham III, PhD Department of Community Medicine School of Primary Medical Care University of Alabama in Huntsville 109 Governors Drive, SW Huntsville, AL 35801

36th Annual Symposium on Fundamental Cancer Research "Cancer Invasion and Metastasis" is the topic of the 36th Annual Symposium on Fundamental Cancer Research sponsored by the University of Texas M.D. Anderson Hospital and Tumor Institute. The symposium will be held March 1-4, 1983 at the Shamrock Hilton Hotel in Houston, Texas. Recent developments in the biology of tumor metastasis formation and possible contributions of these developments to cancer treatment will be explored at the symposium. For additional information, contact Office of Conference Services, Box 18, M.D. Anderson Hospital and Tumor Institute, 6723 Bertner Avenue, Houston, TX 77030 telephone (713) 792-2222.

Call for Abstracts in Nursing Research Abstracts involving current research in nursing are invited for the Second Annual Nursing Research Conference to be held February 25, 1983 at the University of South Florida College of Nursing. Deadline for abstract submissions is September 1, 1982. For more information, write to: Imogene M. King, Chairperson, Committee on Research, University of South Florida Medical Center, College of Nursing, Box 22, 12901 North 30th Street, Tampa, FL 33612. AJPH September 1982, Vol. 72, No. 9

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