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Hindawi Publishing CorporationInternational Journal of DentistryVolume 2010, Article ID 231398, 13 pagesdoi:10.1155/2010/231398

Research Article

New Casemix Classification as an Alternative Method forBudget Allocation in Thai Oral Healthcare Service: A Pilot Study

Thunthita Wisaijohn,1 Atiphan Pimkhaokham,2 Phenkhae Lapying,3 Chumpot Itthichaisri,2

Supasit Pannarunothai,4 Isao Igarashi,1 and Koichi Kawabuchi1

1 Healthcare Economics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University,Tokyo 113-8510, Japan

2 Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Chulalongkorn University, Bangkok 10330, Thailand3 Dental Health Bureau, Department of Health, Ministry of Public Health, Nonthaburi 11000, Thailand4 Faculty of Medicine, Naresuan University, Phitsanulok 65000, Thailand

Correspondence should be addressed to Atiphan Pimkhaokham, [email protected]

Received 27 April 2010; Revised 29 June 2010; Accepted 15 July 2010

Academic Editor: Stephen Richmond

Copyright © 2010 Thunthita Wisaijohn et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

This study aimed to develop a new casemix classification system as an alternative method for the budget allocation of oralhealthcare service (OHCS). Initially, the International Statistical of Diseases and Related Health Problem, 10th revision, ThaiModification (ICD-10-TM) related to OHCS was used for developing the software “Grouper”. This model was designed to allowthe translation of dental procedures into eight-digit codes. Multiple regression analysis was used to analyze the relationship betweenthe factors used for developing the model and the resource consumption. Furthermore, the coefficient of variance, reduction invariance, and relative weight (RW) were applied to test the validity. The results demonstrated that 1,624 OHCS classifications,according to the diagnoses and the procedures performed, showed high homogeneity within groups and heterogeneity betweengroups. Moreover, the RW of the OHCS could be used to predict and control the production costs. In conclusion, this new OHCScasemix classification has a potential use in a global decision making.

1. Introduction

There are many insurance systems worldwide for UniversalHealthcare Coverage. In Thailand, health insurance systemsare categorized into three major schemes: the Civil Ser-vant Medical Benefit Scheme (CSMBS), the Social SecurityScheme (SSS), and the Universal Coverage Scheme (UCS)or the “30 baht (in 2002, 43.0 Baht/US$ copayment) forall diseases” (UCS was implemented in May 2001 andintroduced nationwide in April 2002) [1]. In 2006, the UCSabolished the 30 baht copayment per visit and made the UCSfree [2]. In the past, the health budget was allocated by thecharacteristics of each healthcare provider, the number ofdoctors, and the number of patient beds. Thus, healthcareresources were not equitably allocated between the healthinsurance systems [3]. In 2001, the revamping of the

health insurance system was initiated to restructure themethodology and the system allocating healthcare resourcesby the Health Systems Research Institute [4, 5]. One keydifference between the insurance schemes is that the UCSseparated the provider budget between the inpatient and theoutpatient for exclusive capitation. Under this paradigm, theoutpatient budget was allocated on the basis of the capitationrate, while the inpatient budget was allocated on the basisof Diagnosis Related Group (DRG) within a global budget[1].

Under the UCS, the budget for the oral healthcareservice (OHCS), based on the benefit package, is part ofthe outpatient and Promotion/Prevention budget, to sharefinancial risks among OHCS and general health services.While the budgets for all capital investment budgets areallocated by the Ministry of Public Health (MOPH) regional

2 International Journal of Dentistry

regulators’ judgment, the OHCS, unfortunately, is usuallythe last priority. Therefore, the efficiency of the allocationis doubted, mainly the methodology and reliability aspect,as follows: (1) Since the Universal Healthcare Coveragepolicy was established in the year 2001, the demands forgovernment-funded OHCS have increased significantly. Inparticular, the demand for dental substitution, which ishighly expensive, has increased [6]. The per capita budget forall healthcare service in the fiscal year of 2003 was the sameamount as the previous year, approximately 1,202 baht (43.0Baht/US$) [7, 8] and became 2,202 Baht (34.34 Baht/US$) in2009. (2) Current budget allocation has not been categorizedfor OHCS separately, and there have been few cost studiesof OHCS. The information on the cost of these serviceactivities is scarce, leading to a shortage of information formaking management decisions. Thus, cost studies of OHCSare important and necessary for the evaluation of healthcaremanagerial efficiency and resource allocation, as well as forgenerating the appropriate parameters to use in makingpolicies for healthcare service improvement in the context ofbudgetary constraints [9, 10].

Casemix is a generic term for the patients’ classificationsystem, including inpatient and outpatient status, budgetingallocation, and payment [11]. Successful outcomes from theadoption of a casemix system have been shown in manycountries [12–14]. The best-known classification systemused in a casemix funding model is the DRG. The DRGclassifies acute inpatient episodes into a discrete number ofmanageable categories, depending on their clinical conditionand resource consumption, assigned by a grouper programbased on their demographics, clinical information (diagnosis(Dx) and procedure (Proc) codes), and comorbidity. TheDRG method has been well evaluated for classifying inpatienttreatment [15, 16]. In Thailand, the initial DRG was imple-mented in 1999 and several successive versions have beendeveloped. Pannarunothai’s studies on DRG development inThailand recommended that casemix systems should be usedin the budget allocation for the healthcare service system[11, 17].

In Thailand, most people are covered by the aforemen-tioned insurance schemes, although the budget allocationsand payment systems are different between the schemes.DRG is currently employed as an allocation method onlyfor inpatient healthcare budgets and not for all healthcarebudgets. However, the DRG system has limitations inreflecting the OHCS cost. The future development of theappropriate inpatient and outpatient casemix for OHCS isimportant and necessary for economic healthcare manage-ment [17].

For the above-described reasons, the development ofa new casemix classification in OHCS is desirable. Thisstudy aimed to develop and examine the feasibility of anew casemix classification system as an alternative methodfor budget allocation in Thai OHCS. These three schemeswere each adapted, on the basis of DRG, as an alternativemethod to inpatient and outpatient-related OHCS forbudget allocation [11, 18, 19]. This system might, in time,be applied in other countries healthcare system for OHCSresource allocation as well.

2. Materials and Methods

This study was conducted utilizing the electronic data ofindividual patients treated from April 2008 to March 2009 atthree selected tertiary hospitals that met the study’s inclusioncriteria. The study protocol was approved by the EthicsCommittee of the participating hospitals. The inclusioncriteria were the use of the International Classification ofDiseases, 10th edition (ICD-10) and International Classifi-cation of Disease, 9th edition, Clinical Modification (ICD-9-CM) for the clinical records [20, 21] and the systematickeeping of a database record that was based on a globalcoding of the clinical records. These databases containedinformation on inpatient and outpatient care utilization,including demographic data (date of birth (DOB), age,and gender), clinical information (Dx, Proc), and resourceconsumption (hospital charge, admission date, dischargedate and type, length of stay (LOS), and health insurance).The five main methods used to develop a new casemixclassification as an alternative method for budget allocationof OHCS in this study were coding, classification, costing,calibration, and payment.

2.1. Coding. The coding process was divided into two parts.

Part I. The development of the new casemix classification forOHCS began with the adoption of the ICD-10, ICD-9-CM,Current Dental Terminology 2007 (CDT) [22], InternationalStatistical of Diseases and Related Health Problem, 10threvision, Thai Modification (ICD-10-TM) [23, 24], ThaiDRG version 4 [25], and International Refined DRG (IR-DRG) [26]. The study designed the analysis method in twosteps. Step one, the ICD-10-TM for Dx and Proc codes relatedto OHCS were retrieved by a researcher and approved byfive dentists with more than ten years of clinical experienceand specialists in OHCS. Step two, these codes were thenmapped to ICD-10 for Dx and ICD-9-CM for Proc byProgram Map version 1.0, copyright of Thai health codingcenter, Cluster for Health Information Division, Bureau ofPolicy and Strategy, MOPH, Thailand. These selected codeswere used as inclusion lists of principal diagnoses (PDx) andprocedures (Proc).

Part II. The electronic data of individual patients from thethree selected tertiary hospitals were checked based on theinclusion lists of PDx and Proc (Figure 1).

2.2. Classification. To develop a new casemix classificationsystem for Thai OHCS, a specially designed computer soft-ware program called “Grouper” was used. Grouper was ableto allocate each episode to a DRG according to the clinicalinformation and other relevant data. This program usedclinical and demographic data as the input and produced acorresponding DRG as the output [27].

The OHCS casemix classification model (Grouper) con-sisted of one procedure in one visit that classified cases intotwo main groups: the oral and maxillofacial surgery (OMFS)group, designated M, and the tooth and periodontium

International Journal of Dentistry 3

All patient data

Inclusion lists byprincipal diagnosis

Inclusion lists byprocedure

Oral and maxillofacialsurgery (M)

Tooth andperiodontium (D)

Ungroupable

Ungroupable

No

Yes

No

Yes

Yes Yes

Figure 1: Diagram presents the steps one and two.

1 2 3 4 5 6 7 8

1

2 3

4 5

6

7

8

Represents the two main groups of oral and maxillofacial surgery, designated M, or tooth and periodontium, designated D.

Represents the procedure clusters using anatomy group by body region as shown in ICD-10-TM.

Represents the subgroups of procedure clusters by root operation as shown in ICD-10-TM.

Represents the level of the complexity; 1 (one) for a simple procedure, 2 (two) for a complex procedure, and 3 (three)

for a multidisciplinary or complex procedures.

Represents the general anesthesia (GA); 1 (one) represented GA and 0 (zero) was non-GA.

Represents the complication and comorbidity (CC); 1 (one) was CC and 0 (zero) was non-CC.

For examples: M0802311 (maxilla, operative procedures, level 3, with GA and with CC)D0527200 (tooth restoration, resin-based composite restorations, level 2, without GA and without CC)

Figure 2: Diagram presents the casemix classification model of OHCS (one procedure in one visit) as represented in eight-digit codes.

group, designated D. Multiple procedures in one visit weredesignated as P. The variables used for the OHCS groupingwere included (1) PDx, (2) secondary diagnosis (SDx), (3)Proc (which were classified by the level of complexity bythe same expert group), (4) anatomy group by body regionsrelated to ICD-10-TM (Table 1), (5) root operation relatedto ICD-10-TM (Table 2), (6) general anesthesia (GA), and(7) complication and comorbidity (CC), using the Charlsonindex. This classification system was developed to allow thetranslation of dental procedures into eight-digit codes assummarized in Figures 2, 3, 4, and 5.

2.3. Costs. Initially, multiple regression analysis was usedto study the relationship between the factors used fordeveloping the codes and resource consumption. Thisanalysis was employed to explore the relationship betweenthe cost of P, D, and M (dependent variables) and sev-eral independent variables including, GA, CC, number ofprocedures in one visit, Proc (separated by the level ofcomplexity), and LOS. Cross-validation was used to test thevalidation of the casemix in a separate set of data. Cross-validation showed the quality of the prediction equationbetween the structure data and the data for validation.


Table 1: Examples of both groups (M and D), split into procedureclusters using the anatomy group by body region.

OMFS groups (anatomical body region) Code M

Scalp

Include: scalp and subgaleal soft tissues M01

Skull M02

Cranial nerves X, Trigeminal nerve M03

Cranial nerves X1, Accessory nerve M04

Cranial nerve XII, Hypoglossal nerve M05

Cranial nerves M06

Face M07

Maxilla M08

Mandible M09

Tooth and periodontium group(anatomical body region)

Code D

Oral examination D01

Radiographs/Diagnostic imaging D02

Preventive dentistry D03

Oral hygiene instructions and counseling D04

Tooth restoration D05

Endodontic treatment D06

M: oral and maxillofacial surgery (OMFS) corrects a wide spectrum ofdiseases, injuries, and defects in the head, neck, face, jaws, and the hard andsoft tissues of the oral and maxillofacial region.D: tooth and periodontium: periodontium refers to the specialized tissuesthat surround and support the teeth (small, calcified, whitish structuresfound in the jaws (or mouth)) maintaining them in the maxilla andmandible.

Table 2: Examples of the procedure clusters, split into subgroups ofprocedure clusters using a root operation.

OMFS groups (root operation) Code M

Scalp

Include: scalp and subgaleal soft tissues M01

Diagnostic procedures and non-operativeprocedures

M0101

Operative procedures M0102

Miscellaneous procedures M0104

Other procedures and operations M0199

Tooth and periodontium (root operation) Code D

Oral examination D01

Oral examination procedures D0121

Radiographs/Diagnostic imaging D02

Intraoral film D0222

Extraoral film D0223

Others D0299

M: oral and maxillofacial surgery (OMFS).D: tooth and periodontium.

The higher confidence obtained from the cross-validation,the more suitable the estimation of the population predictionequation.

2.4. Calibration. Three statistical analyses, the coefficient ofvariation (CV), the reduction in variance (RIV), and therelative weight (RW), were applied to verify the minimumvariation within each group, the maximum variation amonggroups, and the assignment of a payment weight for the newcasemix classification, respectively.

The CV is calculated as the standard deviation dividedby the arithmetic mean. The CV value demonstrates thehomogeneity of the cases within each group. A high CVindicates wide variation within each group. The acceptedstandard for CV is that each class should have a CV ofless than 1.0 [15]. The expected end results using the newgrouper program are groups of cases that are clinicallysimilar and/or homogeneous with respect to resource use.

The RIV statistic is commonly used to assess the overallperformance of the grouping method by comparing thevariances of cost before and after grouping. The RIV wasalso related to the amount of variation within the datathat requires explanation. A higher RIV reflected betterperformance of the grouping.

The RW is a measure of the resources used: it comparesthe average resource used in each group with the averageresource used in all cases. In this study, statistical outliersbeyond three standard deviations of the average cost for eachOHCS classification were eliminated [28–30]. The RW wascomputed based on the cost data. It was defined in our studyas the mean cost in each group divided by the mean costs ofall patients. The cost in this study focused only on the costof surgery, including general and local anesthesia, medicaldevices and instruments, and medical supplies. The staff costwas not included in this study because it was not paid ona per case basis. In Thailand, all staff salary is paid by thegovernment and is dependent on the degree of education andthe years of experience. Furthermore, the standard of staffcost has not been well established in Thailand.

2.5. Payment. A payment calculation was necessary toestablish the prospective payment system. The paymentwas calculated using the RW of the OHCS classificationmultiplied by the current reimbursement rate (average baserate) in each group.

3. Results

3.1. Coding

Part I (Steps One and Two). The ICD-10-TM, consistingof 813 diagnoses and 1,090 procedures related to OHCS,were retrieved and mapped to ICD-10 and ICD-9-CM,respectively.

Part II. The electronic data of individual patients fromthree selected tertiary hospitals were checked by an inclu-sion list of PDx and Proc. There were 16,165 (84.64%)cases out of 19,098 initial cases (cases with incompletedata were eliminated) that met the inclusion criteria. Thenumber of inpatient and outpatient cases were 2,709 (16.8%)and 13,456 (83.2%), respectively. The demographic details,


Overviews of Thai oral healthcare service (OHCS)

casemix classification model:

one procedure in one visit

Oral healthcare service(OHCS)

OMFS(M)

Procedures

Tooth andperiodontium

(D)

Oral examinationD01

D02

D03

D04

etc

Level ofcomplexity

Oral examinationprocedure D0121

D01211

D01212

D01213

W GAD012111

W/O GAD012110

W CC D0121111

W/O CC D0121110

W CC D0121101

W/O CC D0121100

Scalp M01

M02

M03

M04

etc

Diagnostic proceduresand non-operative M0101

Level ofcomplexity

M01011

M01012

M01013

W GAM010111

W/O GAM010110

W CC M0101111

W/O CC M0101110

W CC M0101101

W/O CC M0101100

Figure 3: Diagram demonstrates the overviews of the Thai oral healthcare service (OHCS) casemix classification model of one procedurein one visit. M = oral and maxillofacial surgery (OMFS), W = with, W/O = without, GA = general anesthesia, CC = complication andcomorbidity.

1 2 3 4 5 6 7 8

1

2

3

4

5 6

7

8

Represents multiple procedures in one visit designated, P.

Represents the number of total procedures in one visit

Represents the number of oral and maxillofacial surgery (M) procedures.

Represents the number of tooth and periodontium (D) procedures.

Represents the relationship between the levels of complexity (equal to one procedure in one visit)

that was generated by the running software.

Represents the general anesthesia (GA); 1 (one) represented GA and 0 (zero) was non-GA.

Represents the complication and comorbidity (CC); 1 (one) was CC and 0 (zero) was non-CC.

For an example: P2110210 (two procedures in one visit, M and D, complexity level 3 and 2, with GA and without CC)

Figure 4: Diagram presents the casemix classification model of OHCS (multiple procedures in one visit) as represented in eight-digitcodes.

clinical information and health insurance showed that themajority of the patients were female (8,723 cases; 54.0%),non-GA (13,911 cases; 86.05%), non-CC (15,708 cases;97.17%), and CSMBS (6,368 cases; 39.4%).

3.2. Classification. The new OHCS casemix classificationmodel (Grouper) consisted of two major procedure cat-egories, M and D, 62 procedure clusters (Table 1), 165subgroups of procedure clusters (Table 2), and 1,624 OHCS


Overviews of Thai oral healthcare service (OHCS)casemix classification model:

multiple procedures in one visit (P)

Oral healthcareservice (OHCS)

Total procedures ofOMFS (M)

andtooth-periodontium(D)

Two proceduresin one visit

M, M

D, D

M, D

P220

P202

P211

Relationship betweenlevel of complexity

3, 3

3, 2

3, 1

2, 2

2, 1

1, 1

P22001

P22002

P22003

P22004

P22005

P22006

W GAP220011

W/O GAP220010

W CCP2200111

W/O CCP2200110

W CCP2200101

W/O CCP220100

Figure 5: Diagram demonstrates overviews of the Thai oral healthcare service (OHCS) casemix classification model of multiple proceduresin one visit (P). M = oral and maxillofacial surgery (OMFS), W = with, W/O = without, GA = general anesthesia, CC = complication andcomorbidity.

classifications according to the treatment procedures (AnnexTable 5). Each OHCS classification described a cluster ofpatients with related diagnoses, requiring a similar exam-ination and incurring similar treatment costs. There were16,165 patients who were grouped into OHCS classificationsby the grouper software. After the grouping process, only 307OHCS classifications were achieved to cover these procedurecodes. This result was likely limited by the OHCS data, asthe amount available in this pilot study was not sufficient tosupport the OHCS grouper.

3.3. Costs. For predicting costs, regression analysis wasemployed. Table 3 presents the determination of the cost ofP, D, and M. Because cost did not present a normal distribu-tion, a normal logarithmic transformation was undertaken.The predicted cost of P, D, and M had R2values of 0.892,0.132, and 0.122, respectively, and the probability of the F-test statistic was 0.000. The results showed that the GA, CC,number of procedures in one visit, Proc (divided by the levelof complexity), and LOS were associated with the costs of P,D, and M.

3.4. Calibration. To ensure that the OHCS classificationsreflected resource homogeneity within groups and hetero-geneity between groups, the CV and RIV, respectively, wereused for analysis.

The lowest CVs relative to the outpatient groups for P,D, and M were 0.02 (P2110200), 0.01 (D0843200), and 0.19(M4004100), respectively, while the highest CVs relative tothese groups for P, D, and M were 0.87 (P2020500), 0.99(D0736200), and 0.83 (M4310101), respectively. The lowestCVs relative to the inpatient groups for P, D, and M were 0.31

(P2200511), 0 (the number of cases was less than five), and0.22 (M4004100), respectively, while the inpatients’ highestCVs for P, D, and M were 0.98 (P2200410), 0 (the number ofcases was less than five), and 0.99 (M3002110), respectively.Moreover, all OHCS classifications had a CV on cost of lessthan one (Table 4, Annex Table 5).

The RIVs relative to the outpatient groups for P, D, andM were 27, 87, and 65 %, respectively, while the inpatientgroups’ RIVs for P, D, and M were 16, 0 (number of casesless than five) and 22 %, respectively. The results showedthat 100 % of the OHCS classifications had a higher RIV(RIV greater than 0) on cost (Table 4). Both the CV andRIV analysis demonstrated the superior performance of thegrouper software.

The lowest RWs in the outpatient groups for P, D,and M were 0.51 (P3121800), 0.14 (D0222100), and 0.30(M1999101), respectively, while the highest RWs of theoutpatient groups were 3.59 (P3121000), 21.33 (D0843200),and 7.88 (M3002200), respectively. The lowest RWs relativeto the inpatient groups for P, D, and M were 0.13 (P2200600),0 (the number of cases was less than five) and 0.02(M1999100), respectively, while the inpatients’ highest RWswere 2.03 (P3300510), 0 (the number of cases was less thanfive), and 3.46 (M3202210), respectively (Table 4, AnnexTable 5). A high RW indicated a higher case complexity andmore resources required for treatment than for low RWcases. Moreover, RW was the most important result of thecalibration because it was the determinant for the paymentto healthcare providers.

3.5. Payment. This study calculated the base rate character-istics by splitting cases into three main treatment groups


Table 3: Multiple regressions of the cost of multiple procedures (P), the cost of OMFS (M), and the cost of tooth and periodontium (D).

Cost of multiple procedures (P) Cost of OMFS (M) Cost of tooth and periodontium (D)

95% CI 95% CI 95% CI

Odds-ratio Lower-upper P-valueOdds-ratio

Lower-upper P-value Odds-ratio

Lower-upper P-value

GenderMale(reference)

Female 0.97 0.89−1.13 .973 1.14 1.02−1.19 .009 0.95 0.88−1.03 .243

Age0−22(reference)

23−40 1.04 0.89−1.22 .671 1.56 1.39−1.74

8 International Journal of DentistryT

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International Journal of Dentistry 9T

abl

e5:

Con

tin

ued

.

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crip

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10 International Journal of DentistryT

abl

e5:

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tin

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was reliable and could possibly be utilized as a first version.However, many points need to be discussed.

4.1. Coding. In Thailand, the healthcare system has imple-mented ICD-10 for Dx and ICD-9-CM for Proc for manyyears. Recently, ICD-10-TM has been implemented to pro-vide more details in the OHCS coding by modifying itwith International Classification of Disease to Dentistry andStomatology (ICD-DA) for Dx and CDT for Proc. The mostrecent modification of the codes was finished in 2003 [23,24]. Thus, the ICD-10-TM provides an advantage in termsof accuracy and comprehensive coverage for Dx and Proc. Itis unique, but similar to the CDT and matched with Proc oneby one while ICD-9-CM has many Proc for each code. Thesedifferences indicate that ICD-10-TM was suitable, althoughchallenging, for this study.

4.2. Classification. The development of the new casemix clas-sification for OHCS concentrated on practical application ofthe IR-DRG that was instituted by 3M Health InformationSystem. IR-DRG was able to classify inpatient and outpatientstatus and was also useful for appraising the potential forreplacement on both short stay and ambulatory treatments[31]. Taking this fact into consideration, the new casemixclassification was classified by the nature of the patients’procedures rather than by their diagnoses. This classificationsystem was developed to allow the translation of dentalprocedures into eight-digit codes using various variables toobtain a homogenous resource group.

4.3. Costs. There are a variety of methods for estimating theprovider’s cost. However, each method has limitations. Thecost of OHCS varies depending upon several factors: thecharacteristics of the OHCS, the scope and complexity ofthe treatment, the specialty and experience of physician, thehigh investment cost, and the location of the practice. It wasdifficult to measure the total submitted charges for cases ineach OHCS classification. Therefore, the cost in this studywas calculated based on the use of the patient payment toestimate the provider’s cost.

It was expected that this method would be suitable tocalibrate the OHCS classification in this study. However,a future study using a resource-based relative value scale(RBRVS) would be recommended. Because RBRVS is aschema that is used to calculate what medical providersshould be paid, it assigns a relative value unit (RVUs) toeach physician service that is based on three items: thephysician, the practice expense, and the malpractice expense.This schema is currently used by Medicare in the UnitedStates and by nearly all Health Maintenance Organizations(HMOs) [32–34]. In 2009, Relative Value Studies Incorpo-rated (RVSI) of Denver, Colorado developed Relative Valuesfor Dentists (RVD), a RBRVS for dentistry that is currentlyindexed to the Current Dental Terminology (CDT) andsupplemented by additional coding as recommended bypracticing dentists [35].

4.4. Calibration and Payment. Because of the wide range ofcosts among the set of P, D, M, inpatient, and outpatientcategories, the weight should reflect the relative cost ofproviding care and the health resources required in eachDRG. A CV of less than one and a higher RIV wouldbe expected in the new OHCS classification. Thus, theRIV, RW, and base rate characteristics were split into threemain treatment groups consisting of P, D, and M and twopatient groups consisting of inpatient and outpatient for thefollowing reasons.

(i) The higher RIV reflects the better performance of thegrouping. However, P had the lowest RIV in bothinpatient and outpatient groups because P includedmultiple procedures in one visit, and in some casesP had M and D in one visit. P also had more dataand variation than did the other groups. The RIVof M of the inpatient group was lower than the RIVof M of the outpatient group because all inpatientprocedures were major surgeries with GA, complex,and costly procedures. D had the highest RIV inthe outpatient group because all procedures in theoutpatient group were minor surgeries, tooth andperiodontium treatment, without GA.

(ii) The procedures of each group were different accord-ing to the anatomy group, root operation, and totalprocedures in one visit.

(iii) Elementary procedures in the OHCS were wildlydifferent in each group. The OHCS was mostlyfocused on handiwork. OHCS was intensively skilled,time-consuming labor with a high investment costfor the equipment, instruments, and materials [36].

Taken together, this new OHCS grouper has beenpotentially implemented in Thailand. However, in somecountries that use ICD-10 for Dx, ICD-9-CM for Proc andDRG for budget allocation, this information of the mappingprocess could be used as a guideline to further develop theirown systems. Moreover, the benefits of the DRG grouper forOHCS could be used for expenditure estimation, resourceallocation, payment, and oral healthcare finance focus.

4.5. Limitation. The problems related to the implementationof a new casemix classification for OHCS are elaborated asfollows.

(i) Although computerized information systems arewidely used in Thailand, only a few hospitals pro-vided good clinical data that were ready to use [11].This was an important issue for OHCS informationbecause ICD-10 and ICD-9-CM were not commonlyused for recording with the same standardization.This problem was likely due to the limited data fromOHCS in Thailand, which could not support anOHCS grouper.


(ii) The diagnoses and procedures of the OHCS codingsystem are scattered throughout the ICD-10 andICD-9-CM, making them difficult to find and use.Moreover, currently OHCS codings are not beingused effectively in dentistry.

(iii) The OHCS patient data from this study did notinvolve patients from university hospitals becausethe patients in university hospitals had more het-erogeneity in their diagnoses and services than didother hospitals. In addition, OHCS classificationsubgroups may be needed to more precisely describethe resource consumption in the university hospitalsetting.

(iv) There were 1,624 OHCS classifications. In this pilotstudy, the data were limited and were not sufficient tosupport the OHCS grouper. A large number of OHCSclassifications might be difficult to handle as a goodpayment tool. In the future, the OHCS classificationshould decrease the number of groups to providemore efficiency and effectiveness in payment.

(v) OHCS cost weights were calculated using only costdata from some areas that might not be applicableto all hospitals in Thailand. This indicated that anexpanded number of cases and data from additionalhospitals would give a more exact cost weight.

5. Conclusion

This study demonstrated the validity of the new OHCSclassification, showing high homogeneity of the cases withineach group and heterogeneity of the cases between eachgroup. Furthermore, it could be used to predict andcontrol production costs. Therefore, this OHCS casemixclassification has the potential to be used in global decision-making in the future. Moreover, some countries using ICD-10 for diagnoses, ICD-9-CM for procedures, and DRGgrouper for budget allocation might be able to apply thismapping process as a guideline to develop their own system,which might benefit from the use of the DRG grouper forexpenditure estimation, resource allocation, payment, andhealthcare finance focus.

Acknowledgments

The authors would like to thank Monbukagakusho schol-arship and Chulalongkorn University Centenary AcademicDevelopment Project for support this research. They alsothank the staffs of Chonburi, Phrapokklao Chantaburi, andQueen Sirikit hospitals that provided helpful data han-dling, Assistant Professor Yosananda Chantravekin and Dr.Kanjana Singkharotai for reviewing the ICD-10-TM codesrelated to OHCS, and Mr. Monthon Buakaew from MOPHfor grouping. Finally, thanks are due to an anonymousperson for helpful comments on the paper.

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