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A proposed predictive model for assessing dental clinical performance
Khalid M. Al Balkhi, BDS, MS
College of Dentistry King Saud University, Riyadh, Saudi Arabia
The aim
of this article is to present a theoretical model for predicting
patients clinical capacity for dental specialties from a practical
point of view. A detailed mathematical model was presented, with
several examples of its application. Predicting the exact number of
any clinical capacity is an impossible task due to the complexity of
the influencing factors. However, the model can be utilized with
personal customization to provide a predictable educated guess of the
clinical capacity for any clinician or specialty. It may provide
useful information for planning, management, and evaluation of dental
services.
Planning
and management are vital factors in dental services. The majority of
articles related to planning of dental health services are directed
towards the prevention10-17
Very few articles are directed towards evaluating the clinical
capacity of a general or a specialized dental care.18-19
Therefore,
the aim of this article was to present a predictable model for
assessment of the clinical capacity and performance of dental
specialists
Mathematical
Model
The
suggested model is composed of time scale values of different
treatment items per clinician.
Description
of the developed introduction/ module
• Clinical
capacity per clinician
This
represents the total number of patients treated
by a clinician in any dental specialty per year in a private sector
or a government sector.
• Number
of patients per hour
This
variable represents the number of patients seen per hour. It is
related to the average length of time for a patient per visit. It
ensures the customization of the optimal length of treatment
procedure per visit, in accordance with the skills and speed of the
clinician as well as the required time by specialty practice. For
example, an orthodontist may see one patient per hour or two patients
per hour. On the other hand, a prosthodontist may see one patient
every 1.5 hours, whereas a maxillofacial surgeon may see one patient
every 2 hours for complete surgical procedures.
• Number
of hours per session
This
variable represents the number of clinical hours for each clinician
per session, which may vary from one clinic to another.
• Number
of sessions per week
This
variable represents the number of clinical sessions for each
clinician per week.
• Number
of weeks per month
Even
though the average number of weeks per month is 4 weeks, however,
this variable represents the number of weeks per month that the
clinician utilizes for clinical treatment.
• Number
of months per year
Even
though a year consists of 12 months, this variable represents the
number of months per year that the clinician utilizes for clinical
treatment after taking into consideration nonclinical months and
vacations.
• Total
number of visits from start to finish per patient
This
variable represents the average number of visits required to finish
one patient or one case. This varies from one specialty to another.
For example, an orthodontist requires an average of 24 visits (2
years) to complete a case seeing the patient once a month. On the
other hand, an endodontist requires an average of 2 visits per case
(tooth).
• Number
of years per patients
This
variable represents the average number of years required to complete
or finish a case. For the majority of dental specialties which
require one year or less to complete a case, the variable will be
"1." However, for specialty such as orthodontics, it requires an
average of two years to complete a case, therefore the variable will
be "2."
The
model can be summarized and compared as follows:
CCPC
= (P x H x S) / N
Where
CCPC = clinical capacity per clinician per year
P =
number of patients per hour
H =
number of hours per session
S = Total
number of sessions per clinician
work in a year
N = Total
number of visits of a patient
The
summarized model is presented in following examples with an actual
model.
Examples
of the model applications
The
following applied examples represent the result of such module.
EXAMPLE
(I):
An
orthodontist examines 2 patients/hour. He has 6 clinical
sessions/week. The duration of each session is 3 hours. He works 4
weeks/month, 10 months/year. Each patient requires 2 years of
treatment for completion. [The orthodontist needs an average of 24
visits to treat a case].
Therefore,
EXAMPLE
(II):
An
endodontist treats 1 patient/hour. He has 6 clinical sessions/week.
The duration of each session is 3 hours. He works 4 weeks/month, 10
months/year and he needs an average of 2 visits to treat a case
within less than a year.
Therefore,
EXAMPLE
(III):
A
full time pedodontist who treats 1 patient/hour. He has 10 clinical
sessions/week and the duration of each session is 4 hours. He works 4
week/month, 10 months/year and he needs an average of 5 visits to
treat a case within less than a year.
Therefore,
EXAMPLE
(IV):
If
we consider a periodontal patient for nonsurgical periodontal
therapy, a periodontist treats 1 patient per hour. He has 6 clinical
session per week, the duration of each session is 3 hours. He works 4
weeks per month, 10 months/year and he needs an average of 6 visits
to treat a case.
A
periodontist will complete 120 patients per year by ACCPC and SCCPC
model. So the author would recommend the CCPC model due to the same
outcome of both models.
It
is extremely difficult, if not impossible, to consistently predict
the exact number of patients that could be treated by different
clinicians in any specified dental specialty. The following
influencing factors are the reasons behind such difficulty.
1. The
variation between clinicians of the same specialty in their skills,
speed and performance.
2. The
variation in the severity or complexity of the cases which is
reflected in the time needed for treatment.
3. The
variation in the nature of the make up of the clinical patients since
there is always a new influx of patients who may or may not go
through the treatment from start to completion since some are only
for consultation or others may transfer.
4. The
variations in the clinical time and schedule available for each
clinician which differ from one clinic to another.
If
that is the case within the same specialty, then it is more of a
challenge to consistently predict the exact number of patients
treated in different specialties. Since in the latter case, we are
adding another variable or influencing factor which is "different
specialties have different time requirements." Therefore, if such a
task is impossible, then achieving an educated guess in predicting
the number of patients treated by a clinician may be the second best
choice from a practical point of view.
Even
though the module cannot take into consideration all the influencing
factors in their exact details, the clinician, however, has the
ability to customize the module by considering the minimal number of
patients per hour or increasing the total number of visits to finish
a case according
to
his experience. This will account for influencing factors such as
skill, speed and efficiency of the clinician, in addition to the
severity of the cases and the make up of his clinical patients.
In
Example No. (I), the total number of visits for an orthodontic
patients is 24 and it is extended over an average period of 2 years.
During any given year, new patients are seen for consultation or
starting treatment. Then we can conclude that at any given year, the
orthodontist can accommodate up to 144 patients. However, the number
of patients that an orthodontist will finish every two years will be
120 patients or 60 patients yearly due to the overlap of new patients
and old patients at any given year.
In
Example No. (II), an endodontic case means an endodontically treated
tooth or patient requiring one tooth to be endodontically treated.
Since an endodontically treated case requires less than one year to
complete, therefore, in every year, the endodontist has the capacity
to treat and complete 360 new cases.
The
outcome of the module can be helpful in providing a theoretical
justification for the concept of "dental prevention" versus
"dental treatment." This could be explained by the following
hypothetical example.
If
30 percent out of a population of 10 million people require a
specific dental specialty service* like, pedodontic treatment, and
there are 500 full time pedodontic clinicians, utilizing the outcome
of pedodontic Example No. Ill, then we will come to the following
conclusions:
1. We
have 3 million patients needing pedodontic treatment.
2. The
predicted number of patients that could be treated and completed by
the 500 full time clinicians will be 160,000 patients [from Example
III] 320 patients x 500 clinician = 160,000 patients/year.
3. If
we need to treat all 3 million patients, then we need 3,000,000
= 9375 full time clinicians.
3204
4. If
we take into consideration, on one hand, the continuous increase in
population, awareness, needs, demands and cost, and on the other
hand, the limitations in manpower and resources, then it will be very
obvious that "prevention" is the key factor in dental management
rather than "treatment," and a waiting list in dentistry is a
non- avoidable reality.
Finally,
establishing an exact prediction seems to
be an impossible task; however it would be of importance to test the
practical reliability of such theoretical module against actual
clinical data obtained from different dental specialties.
The
presented theoretical module could be helpful in the planning and
management of any dental care providing sector, whether government or
private. It could be used as a practical instrument in predicting the
clinical capacity or performance, as well as an evaluation tool of
the productivity of clinicians within a clinical set up.
Clinical
testing of such module is necessary by future studies.
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Address
reprint requests to:
Dr.
Khalid M. Al Balkhi
PO
Box 60169, Riyadh 11545, Saudi Arabia
email:
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