A Method for the Measurement of Outpatient Resource Utilization ALIREZA
HOOSHIARI, PhD, REZA KHORRAMSHAHGOL, PhD
A method for measuring outpatient resource utilization in terms of the amounts of time different categories of patients spend with various providers is described. Patients are categorized based on selected attributes, but other attributes could be used. The method is based on two important and measurable variables. 1) frequencies of usage of different resources (e.g., nurse practitioners, physicians, x-ray), and 2) amounts of time used, by each provider type and by ancillary services (x-ray). Using the quantitative measures described, an algorithm is developed for measuring the direct labor costs of delivering primary care to different types of primary care patients. Key words resource utilization measurement, hos-
pital management. (Med Decis Making 1992;12:15-21)
Understanding of resource consumption is vital to the successful planning and management of the operation of an outpatient primary care facility. Hospital managers should be able to predict accurately the services needed by the population of patients who have various requirements. The diversity in resource utilization can be measured from a cost standpoint in terms of provider time. In a labor-intensive operation such as an outpatient system, provider time is not only the most expensive resource but also critical because of the supplies and facilities utilized by different categories of patients. This study was based on two important and measurable variables: 1) the frequencies of usage of different
Measuring the probabilities of the encounters of patients with different provider types and ancillary services helps planners to identify how frequently the outpatient resources are used by different categories of patients. For example, not all patients from a certain category who arrive at the outpatient clinic use physicians or nurses (instead, they may see only nurse practitioners). Also, some patients visit the outpatient clinic only for ancillary services (e.g., x-ray, laboratory, or therapy). Their numbers are valuable in outpatient planning and rate setting, and may also be used by third-party payers such as Blue Cross. For example, consider a diagnostic categoiy such as hypertension, for which the x-ray department is not usually used. The small probability of a patient-x-ray encounter can be incorporated into the calculation of the direct labor cost per visit of delivering care ($ per patient) to the category of hypertension patients. It is difficult to accurately estimate the expected workloads for different provider types and ancillary departments because of 1) the stochastic nature of arrivals at the facility by different patient categories and 2) the stochastic movement of patients through the facility. The flows of patients in different categories can be put into matrices containing the probabilities of their encounters with different provider types and the amounts of time they require. These matrices can help management trace the effects of treating different patients on the workloads of the various providers in the system. The matrices may also be used to calculate the average direct labor costs of delivering care to different patient categories and to make estimates in the event that a new case mix is imposed on the facility. This study therefore attempted to do the following: 1) investigate how different provider types and ancillary services (physicians, nurses, nurse practitioners, pharmacy, x-ray, laboratoiy) are used by different classes
(nurse practitioners, physicians, x-ray, etc.); and 2) the amount of the time of each provider type (e.g., physicians) and ancillary services (e.g., x-ray) used by patients. This method offers an approach different from the currently used outpatient case-mix measurements. First, the two variables can be accurately measured. Second, they can be directly related to the outpatient direct labor cost and provider productivity. For example, optimizing provider time spent with patients will increase the number of patients treated in a day, improving productivity. Finally, bottlenecks can be identified and removed for a more robust health care delivery system. The method is very flexible in that it can be used with other similar data. It can be generalized by using attributes other than diagnoses and beneficiary types to classify patients. resources
Received February 14, 1990 fiom the Science and Technology Center, BellSouth Telecommunications, Atlanta, Georgia 30375 (AH) and the Department of Computer Science and Information Systems, The Amencan University, 4400 Massachusetts Avenue NW, Washington, D C 20016 (RK) Revision accepted for publication April 19, 1991
Address
correspondence
and reprint requests to Dr Hooshiari
15
Downloaded from mdm.sagepub.com at Bobst Library, New York University on June 6, 2015
16
of patients; 2) estimate the expected workloads for different provider types and ancillary services (in terms of provider time and frequency of encounter); and 3) estimate the expected direct labor cost per patient visit. The existing literature offers only the most general help in addressing the measurement of resource utilization based on provider time. Most of the information that has appeared is concerned with the way in which providers (mostly physicians) allocate their time,’-3 rather than how much time various classes of patients receive, as is needed to make the estimates described here. One of the earlier studies, by Bergman et all which included data on outpatient visits, attempted to define the activities of pediatricians in seven categories as they worked through their professional day. Based on 149 hours of observation, the authors found that only about 49% of the pediatricians’ time was spent in direct care, while the next largest block of time was devoted to &dquo;other activities concerning patient care.&dquo; A few studies examined the amounts of time that physicians and their assistants spend with patients. O’Bannon, et al.’ and Ekwo et al.6 researched the extents to which a series of factors, some patientrelated (e.g., age, sex, diagnosis) and others systemrelated (e.g., type of visit, day of the week) determined variations in the use of provider time. Using multiple regression, both studies found the major factor to be system-related variables.
Data Sheet (ACDS). The timing sheet included information about the time of arrival of the patient at the facility and the amounts of time the patient spent with different providers. The Ambulatory Care Data Sheet included information such as name, age, sex, and occupation, as well as beneficiary status and actual diagnoses. Data-collection stations were placed in locations where physicans, nurses, and nurse practitioners worked and also in three ancillary departments (x-ray, pharmacy, and laboratory). Upon arrival, each patient was given a pair of these forms, which after completion, were given to the data clerk at the department he or she had visited. Both forms were collected when the patient left. In the ten days of observations, forms were returned for 1,119 patient visits. The information sections on the timing sheet and ACDS were completed with usable results in 95% of cases. The data-collection team enjoyed the full cooperation of the hospital management and the staff. The raw data were stored in files for further analysis. The following information was obtained from the data:
Ambulatory Care
1. Lengths of time that the
ferent providers
patients spent with difthe individual patient visduring
its 2. Probabilities of encounters of different
categories patients with various provider types during individual patient visits of
3. Information such tus of the
Methods of Study The results reported here are based on a study conducted in the Boston Public Health Service (PHS) Hospital in 1981. Four to eight salaried physicians were involved in the outpatient primary care povided by this hospital, depending on the day of the week. The study population was defined as the patients who arrived at the outpatient department of the hospital as either walk-in or appointment patients. This population consisted of both new patients who came for the first time and old (repeat-visit) patients, who often came only to use ancillary services. Even for thennewly-entitled groups such as Southeast Asians and Cuban refugees, this population had been stable for several years. Southeast Asians have had an impact on the Boston PHS Hospital in recent years because of their number and the need for interpreters. The data for these individuals were identified so they could be analyzed in separate categories. Two data-collection sessions, each one week long, were held during the months of January and March 1981. During these periods every effort was made to include data for as many of the patients using the outpatient department as possible. The data for each patient were collected on two forms, which had matching serial numbers: a timing sheet and the PHS
4. Number and
patient
as
age,
sex,
and
beneficiary sta-
patient type(s)
of
diagnosis(es)
that each
received
Categorization of Reputation To aid administrators of an outpatient health care facility to determine the facility’s utilization pattern,
it is necessary to classify patients into different groups. This classification enables the managers to measure the types of services used by different classes of patients and also reveals the patterns of resource consumption associated with various patient categories. The patients were classified, therefore, into different groups according to beneficiary status and diagnoses. First, the patients were classified according to beneficiary status. The beneficiary status of patients was important to the managers of the hospital because of legal questions surrounding the patients’ eligibility for care. Also, because of the special characteristics of the patients associated with various beneficiary groups, beneficiary status can be used as a surrogate variable for several other variables such as occupation, income, and, to some extent, age and sex. Each of the beneficiary classes was known to reflect assorted patterns of disease and health needs.
Downloaded from mdm.sagepub.com at Bobst Library, New York University on June 6, 2015
17
IaN6 1 * Outpatient
Flow Matnces for
Beneficiary Classes and
for Different Numbers of
The second classification of patients was according the types and the numbers of the diagnoses that they received. The diagnostic types in this study were chosen for two reasons. First, the diagnostic types were those used by the providers of the Boston PHS Hospital. Second, a preliminary analysis of variance among the physicans’ service times associated with ten major diagnostic types revealed considerable differences among the service times across major diagnoses. It is, however, recommended that the future research using the methodology of this study consider other diagnostic categories. The categorization according to the type of diagnosis could not be performed on patients with more than one diagnosis (no primary diagnosis was identified by the provider for these patients). Hence, a large number of patients who had multiple diagnoses (almost 50% of the population) had to be excluded from the analysis, which reduced the sample sizes. Also, because of the large number of diagnostic categories (172), it was not possible to find large sample sizes (25 or more) to obtain valid estimates of probabilities and service times (only two diagnostic categories met this requirement). Therefore, it was decided to perform the analysis according to the numbers of diagnoses individual patients received, which reflect, to some extent, the severity of their illnesses. to
Computation of flow and Utilization Matrices Analysis of the data showed that during each outpatient visit providers such as physicians, nurses, nurse practitioners, pharmacists, and x-ray and laboratory technicians were utilized in different frequencies by different categories of patients. Therefore, it was appropriate to measure the work generated for different types of providers throughout the facility as a consequence of treating different classes of patients. The outpatient flow matrix (OFM) introduced in this
Diagnoses
report is intended to provide management with comprehensive information about patient flow. The outpatient flow matrix is a table of proportions that relates different categories of patients (beneficiary and diagnostic) entering the facility to the use of different types of providers. The rows of this matrix correspond to patient categories, while the columns correspond to types of providers and services. The ij entry of this matrix gives the probability that a patient from category i uses provider type j during each visit (encounter probability). Hence,
The statistics available from the outpatient flow matrix show which patient categories generate more work for which types of providers. In other words, one can
discover which patient categories put more pressure on the system by frequent consumption of expensive resources such as physician time. This matrix may also be used to calculate expected numbers of encounters with different provider types (see the next section for
details). Table 1 shows
outpatient flow matrix with nine patient categories (categorized by beneficiary status) and six types of providers. To obtain valid estimates for encounter probabilities, it was estimated that a sample size of at least 25 would be needed for each patient category. Since there is no robust way to identify how big a sample size is needed for this type of estimation (ratio estimation), the sample size of 25 is an
Downloaded from mdm.sagepub.com at Bobst Library, New York University on June 6, 2015
18
TAN 2 9 Outpatient
*0 00898,
or
Utilization Matrices for Various
0.01, hour of nursing
Beneficiary
Classes and for Different Numbers of
time utilized per Amencan seaman per visit.
assumed to be adequate. It should, however, be noted that subjective estimates of encounter probabilities could also be used when available data are not adequate. Only eight patient categories met this requirement, since approximately 60% of the patients who visited the facility during the two weeks of data collection were from these eight categories. Therefore, the remaining categories were aggregated (see table 1) before the estimates for encounter probabilities were obtained. Table 1 also shows the outpatient flow matrix for patients categorized by numbers of diagnoses. Some patients visited the facility only for ancillary services ; hence, they could not be identified with a diagnosis. The providers in the ancillary services did not have the information on patient diagnosis to complete this section of the ACDS form. Therefore, the encounter probabilities in table 1 are for only the fraction of the population whose actual diagnoses were written on their ACDS forms. The next step was to determine the outpatient utilization matrix (OUM) by multiplying each entry of the OFM by the corresponding average time. Let
U,j
=
Diagnoses
the average utilization of j th provider type by the i th patient category during a visit, i 1, 2, m 3, n, j 1, 2, 3,
The outpatient utilization matrix shows the average provider times per patient utilized by different categories of patients during individual patient visits. Table 2 presents the outpatient utilization matrices for patients categorized by beneficiary status and by number of diagnoses. For example, on the average, 0.089 hours of nursing time (per patient) and 0.1647 hours of physician time (per patient) were utilized by American seamen during each visit. The sum of all the entries in one row over six provider types is defined to be the service level for patients of category i. This service level (see equation 2) presents, in fact, the expected total amount of all provider time per patient allocated to patient category i during each visit.
=
=
...
E(T,~) =
...
the average service time of provider treating patients from category i
type j
Therefore,
The
outpatient
provider types
utilization matrix for is then
m
=
6 different
Applications of 01tliti’flt Flow Matrices (OFM and OUM) use made of all provider types, the factors that tend to overload the system as well as the factors that result in underutilization of some provider types. For example, consider the Southeast Asian patients. Although these patients utilize physicians, they require a disproportionate amount of nursing time during their visits (table 1) because of both language problems and particular health and medical problems.
By examining the
one can trace
Downloaded from mdm.sagepub.com at Bobst Library, New York University on June 6, 2015
19
In the week of March 22, 1981 (second data-collection period), a large number of these patients visited the Boston PHS Hospital. This data-collection period was scheduled after the massive immigration of Southeast Asians (Vietnamese and Cambodians, i.e., the Boat People). During that time, they were receiving medical care under legislation that enabled them to enter the United States. Data collection in that period showed that a total of 77 visits were made by patients from this special category, which was 13% of the demand during that period. To compute how much of physician time was utilized by this patient category, the average service time by itself is not adequate. The relative encounter frequency of this category with physicians is also needed. This is because the interest is not only in how long this patient category spends with physicians but also how often it visits physicians. The amount of physician utilization is obtained, therefore, from the outpatient utilization matrix, which reflects both the service time and the frequency of encounters. The average utilization of physicians by this category (Southeast Asians) during each visit, obtained from table 2, is 0.0355 hours per patient. This is well below the overall average utilization of the physicians by the entire population (0.138 hours per patient per visit). The total amount of time that patients from this category spent with different types of providers in the week of March 22, 1981, was computed by multiplying the number of patient visits by this category (77 visits) by the corresponding row in the utilization matrix (table 3). Table 3 indicates that sharp differences existed in the total amounts of time that this patient category spent with different types of providers. For
example, nurses were utilized more (31.7 nursing hours) as compared with physicians (2.73 physician hours). Table 3 also shows that the x-ray department and the laboratory were utilized less than were other ancillary services. On Tuesday, March 23, 1981 (second day of the March data-collection period), when most of the patients who visited the facility were from this category, the x-ray and laboratory facilities were idle most of the time. The utilization and flow matrices may also be used to forecast projected utilization of resources for future planning. This can be shown by constructing the following example. Assume that a total of 200 patient visits by Southeast Asians are expected during the next two weeks. It is essential to determine the effects of their visits on the levels of demand for different provider types and ancillary services. The expected num-
Table 4 . Projected
Numbers of Encounters and Hours
IZMB 3 *
Total Hours Southeast Asian Providers, March 1981
Refugees Spent with
bers of encounters and the total amounts of provider time are found by multiplying the 200 by the rows in the flow and utilization matrices, respectively, associated with this category (see table 4). Approximately 166 encounters will be made with nurses (82.38 hours of nursing time) by this patient category, as compared with 34 encounters with physicians (7.1 hours of physician time). This information may be used to assist managers in staff scheduling for this forecasting horizon. Thus, the management may use this information to reach correct staffing decisions in anticipation of decreasing or increasing patient volume and also in the event a new case mix is imposed on the facility.
Measuring the Direct Labor Cost (per Visit) or Delivering Care The
quantitative measures developed in previous
tions
sec-
be used to calculate the average direct labor costs of the providers who are actually delivering primary care to patients. By using the estimates of the average service times of different provider types and the probability of encounter with each provider type during each patient visit, one can estimate the average direct labor costs per patient per visit incurred by different classes of patients. In this section a method is developed that uses information from the flow matrix and the utilization matrix to measure the expected value of the direct labor cost per patient per visit. This method also provides estimates for the expected direct labor costs incurred by different beneficiary and diagnostic categories in a given visit. Assume that the expected direct labor cost of each patient-provider contact is a linear function of the provider time. Let
d,
can
=
cost of unit time for
provider type j,j
=
1,2,
..., 6
E(C~ )
expected direct labor visiting provider type j
= the
Spent with Providers for
an
Anticipated
cost
of
200 Southeast Asian Patients
Downloaded from mdm.sagepub.com at Bobst Library, New York University on June 6, 2015
a
patient
20
Table 5 o Expected
Direct Labor Costs of Two Patient Categones
Delivering
Care to
diagnostic
P
and
beneficiary
classes. Let
the encounter probability of patients from category i with provider type j during a visit (obtained from OFM) i 1, 2, 3, n,j 1, 2, 3, ... 6
=
=
=
...
E(C~ )
the
expected direct labor cost ($ per patient) per visit of delivering care to a patient from
=
category i
E (T, 7)
average service time of
=
patients The
from
provider type j treating
category
i
direct labor cost of delivering care to from patient category i in a given visit can be obtained using equation 5 for specific category i :
expected
a
Pi
=
E(T~ )
=
probability j (see table
of encounter with
provider type
1)
expected value of the service time of provider type j (see Hooshiari’)
E(C) = the
expected direct labor cost of delivering to a patient per visit ($ per patient)
The average utilization of provider type j by a patient from category i in a given visit, UIJ,may be substituted for PIJE(TIJ) (equation 1). The value of U can be directly obtained from the utilization matrix (table 2). Thus,
care
The
expected direct provider type j is
The
labor cost of
a
patient visiting
direct labor cost per visit of delivering patient ($ per patient) is the sum of the direct labor cost of a patient visiting each expected times the encounter probability with provider type each provider type, or care
expected to
a
Equation 7 may be used to calculate the average direct labor costs ($ per patient) per visit of delivering care to different categories of patients. As an example, consider two different categories of patients, Southeast Asians and dependents of retired Air Force personnel. In table 2
one can see
that the first
category involves
heavy utilization of nurses, whereas the second category utilizes physicians and the x-ray department more than any of the other provider types. The expected costs of delivering care and the encounter frea
shown in table 5. Reasonable estimates hourly wages of providers working for the Boston PHS Hospital at the time the study were obtained from the hospital management. They are:
quencies
are
for
By substituting the expression for E(C~ ) equation 4, one obtains
from
equation
3 into
Nurse = $12/hour Nurse practitioner
$18/hour $43/hour $63/hour X-ray Laboratory = $52/hour Pharmacy = $50/hour
Physician
=
=
=
Therefore, equation 5 reflects the expected direct labor per visit, E(C) ($ per patient) in terms of P~, the probability, the unit time cost d~, and the service time, E(T~ ). By multiplying the average average direct labor cost per patient visit, E(C), by the total number of patients visits in a given period of time, one can obtain the total direct labor cost in that period. The above method can be used to calculate the average direct labor cost per patient visit for particular types of patient separately, i.e., patients classified by cost
encounter
It should be noted that for the
ancillary departments
these costs are the labor costs of operating the facilities. When these values are substituted in the cost equations, the results shown in table 5 are obtained. Table 5 shows the average direct labor costs of delivering care ($ per patient visit) to two different patient categories. These costs are tabulated based on the
Downloaded from mdm.sagepub.com at Bobst Library, New York University on June 6, 2015
21
and then total cost is calculated. These results show that the average direct labor cost per visit ($ per patient) of delivering care to dependents of retired Air Force personnel is 55% more than that of delivering care to Southeast Asians.
providers
provider type,
probabilities 2. The method
Conclusion
objective
It is
possible
to
mul15, min-
use
of n-minute intervals (n = 10, or providers’ service times. For example, can use 2n or 3n for physician time so that
tiples
utes) for one
measures.
keep
track of the time.
and
provider
service times.
can
a
more
patients
per
day.
3. The method
recommended can be applied with other categorization schemes. Other attributes can be used instead of the beneficiary and diagnoses
(e.g., age).
In conclusion, by studying the characteristics of the demand (in terms of utilization of various provider types), it is possible to see whether the utilization of the resources within the clinic are subject to fluctuation from session to session. For example, Nuffied’7 reported (and it was also found in this research) that on many occasions an ancillary service such as x-ray and its expensive equipment were idle for more than half of a working day. This problem can be solved, to some extent, through the organization of the clinic timetable. The clinic timetable can be modified by allocation of providers to half-day sessions to improve the overall clinic efficiency. A detailed discussion of this subject is, however, outside of the focus of this research.
References 1
2 3
4 5
1. Use constant intervals for service times instead
of
can
be expanded from a descriptive prescriptive model when certain optimization rules are used to improve provider productivity. For example, by knowing how much provider time is needed for different classes of patients (categorized according to certain attributes), the clinic scheduling system can be altered to treat to
The outpatient flow and utilization matrices (OFM and OUM) were introduced to estimate the work generated throughout an outpatient facility as a consequence of treating different types of patients (i.e., patients with different diagnoses and beneficiary status). These matrices, which are to some extent measures of performance for the outpatient facility, may be used to trace the effects that changing numbers of patients would have on various resources and the effect of imposition on the facility of a new case mix. They further enable one to discover which provider types are utilized more and by what types of patients. Continuous examination of these matrices can trace factors tending to overload the system and point out underutilization of particular provider types. Also, comparison of current values (encounter probabilities and service times per patient) with those of previous periods provides estimates of the need for increasing capacity or initiating studies to overcome deficiencies. The information about the flow of patients and the utilization of providers, accompanied with proper forecasting techniques, can be used by the clinic management in reaching correct decisions regarding future planning and staff scheduling. In a labor-intensive operation such as an outpatient clinic, labor cost is a major portion of the total cost. This method of computing direct labor costs per patient visit and encounter probabilities for different classes of patients yields information useful to decision makers involved in outpatient rate setting. Such information is also valuable to insurance companies and other third-party payers. Also, due to the importance of provider time as a critical resource and its direct relationship with supplies and facilities, the numbers obtained on average direct labor cost per patient may be used as an indication of how the supplies and facilities are utilized by different classes of patients during their visits. The method can be expanded or altered for use in different settings:
themselves
Subjective probabilities can also be used instead of objective data to estimate both encounter
6
Brody BL, Stokes J Use of professional time by internists and general practitioners in group and solo practice Ann Intern Med 1970,73 741-9 Parrish HM, Bishop FM, Baker OS Time study of general practitioner’s office hours Arch Environ Health 1967,14 892-8 Rising EJ, McBride TC, Ralph JR, Averill BW, Allen DS II, Baron R Time allocation of physicians and productivity In Picket RM, Tripps TJ, eds Human factors in health care Boston Lexington Books 1974 Bergman AB, Dassel SW, Wedewood RJ Time-motion study of practicing pediatricians Pediatrics 1966,38 254-63 O’Bannon JE, Mullooly JB, McCabe MA Determinants of lengths of outpatient visits in a prepaid group practice setting Med Care 1978,16 226-44 Ekwo EE, Dusdieker LB, Bean JA, Daniels MA How lengths of office visits vary when primary care practices employ physician assistants
7
Inquiry 1980 (Summer) 1980,17 145-54 Report Waiting in hospital outpatient departments ford, England Oxford University Press, 1965 Nuffied
Downloaded from mdm.sagepub.com at Bobst Library, New York University on June 6, 2015
Ox-
HOOSHIARI, PhD, REZA KHORRAMSHAHGOL, PhD
A method for measuring outpatient resource utilization in terms of the amounts of time different categories of patients spend with various providers is described. Patients are categorized based on selected attributes, but other attributes could be used. The method is based on two important and measurable variables. 1) frequencies of usage of different resources (e.g., nurse practitioners, physicians, x-ray), and 2) amounts of time used, by each provider type and by ancillary services (x-ray). Using the quantitative measures described, an algorithm is developed for measuring the direct labor costs of delivering primary care to different types of primary care patients. Key words resource utilization measurement, hos-
pital management. (Med Decis Making 1992;12:15-21)
Understanding of resource consumption is vital to the successful planning and management of the operation of an outpatient primary care facility. Hospital managers should be able to predict accurately the services needed by the population of patients who have various requirements. The diversity in resource utilization can be measured from a cost standpoint in terms of provider time. In a labor-intensive operation such as an outpatient system, provider time is not only the most expensive resource but also critical because of the supplies and facilities utilized by different categories of patients. This study was based on two important and measurable variables: 1) the frequencies of usage of different
Measuring the probabilities of the encounters of patients with different provider types and ancillary services helps planners to identify how frequently the outpatient resources are used by different categories of patients. For example, not all patients from a certain category who arrive at the outpatient clinic use physicians or nurses (instead, they may see only nurse practitioners). Also, some patients visit the outpatient clinic only for ancillary services (e.g., x-ray, laboratory, or therapy). Their numbers are valuable in outpatient planning and rate setting, and may also be used by third-party payers such as Blue Cross. For example, consider a diagnostic categoiy such as hypertension, for which the x-ray department is not usually used. The small probability of a patient-x-ray encounter can be incorporated into the calculation of the direct labor cost per visit of delivering care ($ per patient) to the category of hypertension patients. It is difficult to accurately estimate the expected workloads for different provider types and ancillary departments because of 1) the stochastic nature of arrivals at the facility by different patient categories and 2) the stochastic movement of patients through the facility. The flows of patients in different categories can be put into matrices containing the probabilities of their encounters with different provider types and the amounts of time they require. These matrices can help management trace the effects of treating different patients on the workloads of the various providers in the system. The matrices may also be used to calculate the average direct labor costs of delivering care to different patient categories and to make estimates in the event that a new case mix is imposed on the facility. This study therefore attempted to do the following: 1) investigate how different provider types and ancillary services (physicians, nurses, nurse practitioners, pharmacy, x-ray, laboratoiy) are used by different classes
(nurse practitioners, physicians, x-ray, etc.); and 2) the amount of the time of each provider type (e.g., physicians) and ancillary services (e.g., x-ray) used by patients. This method offers an approach different from the currently used outpatient case-mix measurements. First, the two variables can be accurately measured. Second, they can be directly related to the outpatient direct labor cost and provider productivity. For example, optimizing provider time spent with patients will increase the number of patients treated in a day, improving productivity. Finally, bottlenecks can be identified and removed for a more robust health care delivery system. The method is very flexible in that it can be used with other similar data. It can be generalized by using attributes other than diagnoses and beneficiary types to classify patients. resources
Received February 14, 1990 fiom the Science and Technology Center, BellSouth Telecommunications, Atlanta, Georgia 30375 (AH) and the Department of Computer Science and Information Systems, The Amencan University, 4400 Massachusetts Avenue NW, Washington, D C 20016 (RK) Revision accepted for publication April 19, 1991
Address
correspondence
and reprint requests to Dr Hooshiari
15
Downloaded from mdm.sagepub.com at Bobst Library, New York University on June 6, 2015
16
of patients; 2) estimate the expected workloads for different provider types and ancillary services (in terms of provider time and frequency of encounter); and 3) estimate the expected direct labor cost per patient visit. The existing literature offers only the most general help in addressing the measurement of resource utilization based on provider time. Most of the information that has appeared is concerned with the way in which providers (mostly physicians) allocate their time,’-3 rather than how much time various classes of patients receive, as is needed to make the estimates described here. One of the earlier studies, by Bergman et all which included data on outpatient visits, attempted to define the activities of pediatricians in seven categories as they worked through their professional day. Based on 149 hours of observation, the authors found that only about 49% of the pediatricians’ time was spent in direct care, while the next largest block of time was devoted to &dquo;other activities concerning patient care.&dquo; A few studies examined the amounts of time that physicians and their assistants spend with patients. O’Bannon, et al.’ and Ekwo et al.6 researched the extents to which a series of factors, some patientrelated (e.g., age, sex, diagnosis) and others systemrelated (e.g., type of visit, day of the week) determined variations in the use of provider time. Using multiple regression, both studies found the major factor to be system-related variables.
Data Sheet (ACDS). The timing sheet included information about the time of arrival of the patient at the facility and the amounts of time the patient spent with different providers. The Ambulatory Care Data Sheet included information such as name, age, sex, and occupation, as well as beneficiary status and actual diagnoses. Data-collection stations were placed in locations where physicans, nurses, and nurse practitioners worked and also in three ancillary departments (x-ray, pharmacy, and laboratory). Upon arrival, each patient was given a pair of these forms, which after completion, were given to the data clerk at the department he or she had visited. Both forms were collected when the patient left. In the ten days of observations, forms were returned for 1,119 patient visits. The information sections on the timing sheet and ACDS were completed with usable results in 95% of cases. The data-collection team enjoyed the full cooperation of the hospital management and the staff. The raw data were stored in files for further analysis. The following information was obtained from the data:
Ambulatory Care
1. Lengths of time that the
ferent providers
patients spent with difthe individual patient visduring
its 2. Probabilities of encounters of different
categories patients with various provider types during individual patient visits of
3. Information such tus of the
Methods of Study The results reported here are based on a study conducted in the Boston Public Health Service (PHS) Hospital in 1981. Four to eight salaried physicians were involved in the outpatient primary care povided by this hospital, depending on the day of the week. The study population was defined as the patients who arrived at the outpatient department of the hospital as either walk-in or appointment patients. This population consisted of both new patients who came for the first time and old (repeat-visit) patients, who often came only to use ancillary services. Even for thennewly-entitled groups such as Southeast Asians and Cuban refugees, this population had been stable for several years. Southeast Asians have had an impact on the Boston PHS Hospital in recent years because of their number and the need for interpreters. The data for these individuals were identified so they could be analyzed in separate categories. Two data-collection sessions, each one week long, were held during the months of January and March 1981. During these periods every effort was made to include data for as many of the patients using the outpatient department as possible. The data for each patient were collected on two forms, which had matching serial numbers: a timing sheet and the PHS
4. Number and
patient
as
age,
sex,
and
beneficiary sta-
patient type(s)
of
diagnosis(es)
that each
received
Categorization of Reputation To aid administrators of an outpatient health care facility to determine the facility’s utilization pattern,
it is necessary to classify patients into different groups. This classification enables the managers to measure the types of services used by different classes of patients and also reveals the patterns of resource consumption associated with various patient categories. The patients were classified, therefore, into different groups according to beneficiary status and diagnoses. First, the patients were classified according to beneficiary status. The beneficiary status of patients was important to the managers of the hospital because of legal questions surrounding the patients’ eligibility for care. Also, because of the special characteristics of the patients associated with various beneficiary groups, beneficiary status can be used as a surrogate variable for several other variables such as occupation, income, and, to some extent, age and sex. Each of the beneficiary classes was known to reflect assorted patterns of disease and health needs.
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17
IaN6 1 * Outpatient
Flow Matnces for
Beneficiary Classes and
for Different Numbers of
The second classification of patients was according the types and the numbers of the diagnoses that they received. The diagnostic types in this study were chosen for two reasons. First, the diagnostic types were those used by the providers of the Boston PHS Hospital. Second, a preliminary analysis of variance among the physicans’ service times associated with ten major diagnostic types revealed considerable differences among the service times across major diagnoses. It is, however, recommended that the future research using the methodology of this study consider other diagnostic categories. The categorization according to the type of diagnosis could not be performed on patients with more than one diagnosis (no primary diagnosis was identified by the provider for these patients). Hence, a large number of patients who had multiple diagnoses (almost 50% of the population) had to be excluded from the analysis, which reduced the sample sizes. Also, because of the large number of diagnostic categories (172), it was not possible to find large sample sizes (25 or more) to obtain valid estimates of probabilities and service times (only two diagnostic categories met this requirement). Therefore, it was decided to perform the analysis according to the numbers of diagnoses individual patients received, which reflect, to some extent, the severity of their illnesses. to
Computation of flow and Utilization Matrices Analysis of the data showed that during each outpatient visit providers such as physicians, nurses, nurse practitioners, pharmacists, and x-ray and laboratory technicians were utilized in different frequencies by different categories of patients. Therefore, it was appropriate to measure the work generated for different types of providers throughout the facility as a consequence of treating different classes of patients. The outpatient flow matrix (OFM) introduced in this
Diagnoses
report is intended to provide management with comprehensive information about patient flow. The outpatient flow matrix is a table of proportions that relates different categories of patients (beneficiary and diagnostic) entering the facility to the use of different types of providers. The rows of this matrix correspond to patient categories, while the columns correspond to types of providers and services. The ij entry of this matrix gives the probability that a patient from category i uses provider type j during each visit (encounter probability). Hence,
The statistics available from the outpatient flow matrix show which patient categories generate more work for which types of providers. In other words, one can
discover which patient categories put more pressure on the system by frequent consumption of expensive resources such as physician time. This matrix may also be used to calculate expected numbers of encounters with different provider types (see the next section for
details). Table 1 shows
outpatient flow matrix with nine patient categories (categorized by beneficiary status) and six types of providers. To obtain valid estimates for encounter probabilities, it was estimated that a sample size of at least 25 would be needed for each patient category. Since there is no robust way to identify how big a sample size is needed for this type of estimation (ratio estimation), the sample size of 25 is an
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18
TAN 2 9 Outpatient
*0 00898,
or
Utilization Matrices for Various
0.01, hour of nursing
Beneficiary
Classes and for Different Numbers of
time utilized per Amencan seaman per visit.
assumed to be adequate. It should, however, be noted that subjective estimates of encounter probabilities could also be used when available data are not adequate. Only eight patient categories met this requirement, since approximately 60% of the patients who visited the facility during the two weeks of data collection were from these eight categories. Therefore, the remaining categories were aggregated (see table 1) before the estimates for encounter probabilities were obtained. Table 1 also shows the outpatient flow matrix for patients categorized by numbers of diagnoses. Some patients visited the facility only for ancillary services ; hence, they could not be identified with a diagnosis. The providers in the ancillary services did not have the information on patient diagnosis to complete this section of the ACDS form. Therefore, the encounter probabilities in table 1 are for only the fraction of the population whose actual diagnoses were written on their ACDS forms. The next step was to determine the outpatient utilization matrix (OUM) by multiplying each entry of the OFM by the corresponding average time. Let
U,j
=
Diagnoses
the average utilization of j th provider type by the i th patient category during a visit, i 1, 2, m 3, n, j 1, 2, 3,
The outpatient utilization matrix shows the average provider times per patient utilized by different categories of patients during individual patient visits. Table 2 presents the outpatient utilization matrices for patients categorized by beneficiary status and by number of diagnoses. For example, on the average, 0.089 hours of nursing time (per patient) and 0.1647 hours of physician time (per patient) were utilized by American seamen during each visit. The sum of all the entries in one row over six provider types is defined to be the service level for patients of category i. This service level (see equation 2) presents, in fact, the expected total amount of all provider time per patient allocated to patient category i during each visit.
=
=
...
E(T,~) =
...
the average service time of provider treating patients from category i
type j
Therefore,
The
outpatient
provider types
utilization matrix for is then
m
=
6 different
Applications of 01tliti’flt Flow Matrices (OFM and OUM) use made of all provider types, the factors that tend to overload the system as well as the factors that result in underutilization of some provider types. For example, consider the Southeast Asian patients. Although these patients utilize physicians, they require a disproportionate amount of nursing time during their visits (table 1) because of both language problems and particular health and medical problems.
By examining the
one can trace
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19
In the week of March 22, 1981 (second data-collection period), a large number of these patients visited the Boston PHS Hospital. This data-collection period was scheduled after the massive immigration of Southeast Asians (Vietnamese and Cambodians, i.e., the Boat People). During that time, they were receiving medical care under legislation that enabled them to enter the United States. Data collection in that period showed that a total of 77 visits were made by patients from this special category, which was 13% of the demand during that period. To compute how much of physician time was utilized by this patient category, the average service time by itself is not adequate. The relative encounter frequency of this category with physicians is also needed. This is because the interest is not only in how long this patient category spends with physicians but also how often it visits physicians. The amount of physician utilization is obtained, therefore, from the outpatient utilization matrix, which reflects both the service time and the frequency of encounters. The average utilization of physicians by this category (Southeast Asians) during each visit, obtained from table 2, is 0.0355 hours per patient. This is well below the overall average utilization of the physicians by the entire population (0.138 hours per patient per visit). The total amount of time that patients from this category spent with different types of providers in the week of March 22, 1981, was computed by multiplying the number of patient visits by this category (77 visits) by the corresponding row in the utilization matrix (table 3). Table 3 indicates that sharp differences existed in the total amounts of time that this patient category spent with different types of providers. For
example, nurses were utilized more (31.7 nursing hours) as compared with physicians (2.73 physician hours). Table 3 also shows that the x-ray department and the laboratory were utilized less than were other ancillary services. On Tuesday, March 23, 1981 (second day of the March data-collection period), when most of the patients who visited the facility were from this category, the x-ray and laboratory facilities were idle most of the time. The utilization and flow matrices may also be used to forecast projected utilization of resources for future planning. This can be shown by constructing the following example. Assume that a total of 200 patient visits by Southeast Asians are expected during the next two weeks. It is essential to determine the effects of their visits on the levels of demand for different provider types and ancillary services. The expected num-
Table 4 . Projected
Numbers of Encounters and Hours
IZMB 3 *
Total Hours Southeast Asian Providers, March 1981
Refugees Spent with
bers of encounters and the total amounts of provider time are found by multiplying the 200 by the rows in the flow and utilization matrices, respectively, associated with this category (see table 4). Approximately 166 encounters will be made with nurses (82.38 hours of nursing time) by this patient category, as compared with 34 encounters with physicians (7.1 hours of physician time). This information may be used to assist managers in staff scheduling for this forecasting horizon. Thus, the management may use this information to reach correct staffing decisions in anticipation of decreasing or increasing patient volume and also in the event a new case mix is imposed on the facility.
Measuring the Direct Labor Cost (per Visit) or Delivering Care The
quantitative measures developed in previous
tions
sec-
be used to calculate the average direct labor costs of the providers who are actually delivering primary care to patients. By using the estimates of the average service times of different provider types and the probability of encounter with each provider type during each patient visit, one can estimate the average direct labor costs per patient per visit incurred by different classes of patients. In this section a method is developed that uses information from the flow matrix and the utilization matrix to measure the expected value of the direct labor cost per patient per visit. This method also provides estimates for the expected direct labor costs incurred by different beneficiary and diagnostic categories in a given visit. Assume that the expected direct labor cost of each patient-provider contact is a linear function of the provider time. Let
d,
can
=
cost of unit time for
provider type j,j
=
1,2,
..., 6
E(C~ )
expected direct labor visiting provider type j
= the
Spent with Providers for
an
Anticipated
cost
of
200 Southeast Asian Patients
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a
patient
20
Table 5 o Expected
Direct Labor Costs of Two Patient Categones
Delivering
Care to
diagnostic
P
and
beneficiary
classes. Let
the encounter probability of patients from category i with provider type j during a visit (obtained from OFM) i 1, 2, 3, n,j 1, 2, 3, ... 6
=
=
=
...
E(C~ )
the
expected direct labor cost ($ per patient) per visit of delivering care to a patient from
=
category i
E (T, 7)
average service time of
=
patients The
from
provider type j treating
category
i
direct labor cost of delivering care to from patient category i in a given visit can be obtained using equation 5 for specific category i :
expected
a
Pi
=
E(T~ )
=
probability j (see table
of encounter with
provider type
1)
expected value of the service time of provider type j (see Hooshiari’)
E(C) = the
expected direct labor cost of delivering to a patient per visit ($ per patient)
The average utilization of provider type j by a patient from category i in a given visit, UIJ,may be substituted for PIJE(TIJ) (equation 1). The value of U can be directly obtained from the utilization matrix (table 2). Thus,
care
The
expected direct provider type j is
The
labor cost of
a
patient visiting
direct labor cost per visit of delivering patient ($ per patient) is the sum of the direct labor cost of a patient visiting each expected times the encounter probability with provider type each provider type, or care
expected to
a
Equation 7 may be used to calculate the average direct labor costs ($ per patient) per visit of delivering care to different categories of patients. As an example, consider two different categories of patients, Southeast Asians and dependents of retired Air Force personnel. In table 2
one can see
that the first
category involves
heavy utilization of nurses, whereas the second category utilizes physicians and the x-ray department more than any of the other provider types. The expected costs of delivering care and the encounter frea
shown in table 5. Reasonable estimates hourly wages of providers working for the Boston PHS Hospital at the time the study were obtained from the hospital management. They are:
quencies
are
for
By substituting the expression for E(C~ ) equation 4, one obtains
from
equation
3 into
Nurse = $12/hour Nurse practitioner
$18/hour $43/hour $63/hour X-ray Laboratory = $52/hour Pharmacy = $50/hour
Physician
=
=
=
Therefore, equation 5 reflects the expected direct labor per visit, E(C) ($ per patient) in terms of P~, the probability, the unit time cost d~, and the service time, E(T~ ). By multiplying the average average direct labor cost per patient visit, E(C), by the total number of patients visits in a given period of time, one can obtain the total direct labor cost in that period. The above method can be used to calculate the average direct labor cost per patient visit for particular types of patient separately, i.e., patients classified by cost
encounter
It should be noted that for the
ancillary departments
these costs are the labor costs of operating the facilities. When these values are substituted in the cost equations, the results shown in table 5 are obtained. Table 5 shows the average direct labor costs of delivering care ($ per patient visit) to two different patient categories. These costs are tabulated based on the
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21
and then total cost is calculated. These results show that the average direct labor cost per visit ($ per patient) of delivering care to dependents of retired Air Force personnel is 55% more than that of delivering care to Southeast Asians.
providers
provider type,
probabilities 2. The method
Conclusion
objective
It is
possible
to
mul15, min-
use
of n-minute intervals (n = 10, or providers’ service times. For example, can use 2n or 3n for physician time so that
tiples
utes) for one
measures.
keep
track of the time.
and
provider
service times.
can
a
more
patients
per
day.
3. The method
recommended can be applied with other categorization schemes. Other attributes can be used instead of the beneficiary and diagnoses
(e.g., age).
In conclusion, by studying the characteristics of the demand (in terms of utilization of various provider types), it is possible to see whether the utilization of the resources within the clinic are subject to fluctuation from session to session. For example, Nuffied’7 reported (and it was also found in this research) that on many occasions an ancillary service such as x-ray and its expensive equipment were idle for more than half of a working day. This problem can be solved, to some extent, through the organization of the clinic timetable. The clinic timetable can be modified by allocation of providers to half-day sessions to improve the overall clinic efficiency. A detailed discussion of this subject is, however, outside of the focus of this research.
References 1
2 3
4 5
1. Use constant intervals for service times instead
of
can
be expanded from a descriptive prescriptive model when certain optimization rules are used to improve provider productivity. For example, by knowing how much provider time is needed for different classes of patients (categorized according to certain attributes), the clinic scheduling system can be altered to treat to
The outpatient flow and utilization matrices (OFM and OUM) were introduced to estimate the work generated throughout an outpatient facility as a consequence of treating different types of patients (i.e., patients with different diagnoses and beneficiary status). These matrices, which are to some extent measures of performance for the outpatient facility, may be used to trace the effects that changing numbers of patients would have on various resources and the effect of imposition on the facility of a new case mix. They further enable one to discover which provider types are utilized more and by what types of patients. Continuous examination of these matrices can trace factors tending to overload the system and point out underutilization of particular provider types. Also, comparison of current values (encounter probabilities and service times per patient) with those of previous periods provides estimates of the need for increasing capacity or initiating studies to overcome deficiencies. The information about the flow of patients and the utilization of providers, accompanied with proper forecasting techniques, can be used by the clinic management in reaching correct decisions regarding future planning and staff scheduling. In a labor-intensive operation such as an outpatient clinic, labor cost is a major portion of the total cost. This method of computing direct labor costs per patient visit and encounter probabilities for different classes of patients yields information useful to decision makers involved in outpatient rate setting. Such information is also valuable to insurance companies and other third-party payers. Also, due to the importance of provider time as a critical resource and its direct relationship with supplies and facilities, the numbers obtained on average direct labor cost per patient may be used as an indication of how the supplies and facilities are utilized by different classes of patients during their visits. The method can be expanded or altered for use in different settings:
themselves
Subjective probabilities can also be used instead of objective data to estimate both encounter
6
Brody BL, Stokes J Use of professional time by internists and general practitioners in group and solo practice Ann Intern Med 1970,73 741-9 Parrish HM, Bishop FM, Baker OS Time study of general practitioner’s office hours Arch Environ Health 1967,14 892-8 Rising EJ, McBride TC, Ralph JR, Averill BW, Allen DS II, Baron R Time allocation of physicians and productivity In Picket RM, Tripps TJ, eds Human factors in health care Boston Lexington Books 1974 Bergman AB, Dassel SW, Wedewood RJ Time-motion study of practicing pediatricians Pediatrics 1966,38 254-63 O’Bannon JE, Mullooly JB, McCabe MA Determinants of lengths of outpatient visits in a prepaid group practice setting Med Care 1978,16 226-44 Ekwo EE, Dusdieker LB, Bean JA, Daniels MA How lengths of office visits vary when primary care practices employ physician assistants
7
Inquiry 1980 (Summer) 1980,17 145-54 Report Waiting in hospital outpatient departments ford, England Oxford University Press, 1965 Nuffied
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Ox-
