Int J Health Plann Mgmt2003;18: 151–159.
Published online in Wiley InterScience (www.interscience.wiley.com).DOI: 10.1002/hpm.702
Disease coding errors by health care
organizations: effects of a government
quality intervention
Euichul Shin
1*, William H. Dow
2, Arnold D. Kaluzny
2, Yong-Moon Park
1and Kidong Park
31
Department of Preventive Medicine, College of Medicine, The Catholic University, Seoul, Korea
2
Department of Health Policy and Administration, School of Public Health, University of North Carolina, Chapel Hill, USA
3
Health Insurance Benefit Division, The Ministry of Health and Welfare, Korea
SUMMARY
Disease coding errors in claims data can cause serious problems for financing, reimbursement systems, public health surveillance and health research. This study analysed a government intervention to improve coding accuracy of health care organizations in South Korea. The intervention was implemented in 1997 by 226 organizations that had submitted erroneous claims in 1996 for five selected diseases. In 1998, 94% of these organizations eliminated cod-ing errors for these diseases. Those organizations least responsive to the intervention were tertiary hospitals, those publicly owned, and those with other complex organizational charac-teristics. Overall, this simple intervention appeared extremely effective, and wider adoption of such techniques should be explored. Copyright#2003 John Wiley & Sons, Ltd.
key words: claims data; disease coding; health care organizations; public health surveillance
INTRODUCTION
Complex modern health care systems require accuracy in the coding of diseases by health care organizations. Disease information from health insurance claims data underpin diagnosis-based provider reimbursement systems, provide a key compo-nent of public health surveillance, and constitute a major data source for health research. Despite this, most countries focus little attention on monitoring and improving the quality of claims data disease coding.
The present study reports on the results of a 1997 intervention by the South Kor-ean Ministry of Health and Welfare to improve the quality of disease coding on
* Correspondence to: Dr E. Shin, Department of Preventive Medicine, College of Medicine, The Catholic University of Korea 505, Banpo-Dong, Socho-Ku, Seoul 137-701, Korea.
health insurance claims data submitted to the National Health Insurance Corpora-tion (NHIC). NHIC administers the country’s naCorpora-tional health insurance programme that began universal coverage in 1989, and thus receives claims for reimbursement purposes from all health care organizations in the country. Below we analyse both the overall change in coding accuracy following the intervention, as well as the health care organizational characteristics associated with coding changes, to better understand the organizations’ coding behaviour.
Studying such an intervention in the Korean context is salient for a number of reasons. First, Korea has attempted to slow the rising health care costs associated with universal coverage by adopting utilization review programmes and new pay-ment systems such as diagnostic related groups (Shinet al., 1993), and resource-based relative value scales (Kimet al., 1995), which require detailed clinical infor-mation. Korea’s experience with such systems is important to numerous other coun-tries attempting to reform provider payment incentives.
Second, previous research has identified serious problems in disease coding accu-racy within the NHIC’s claims database. Shinet al.(1998) found that out of several notifiable communicable diseases studied, only 10.1% of claims were correctly coded. Similarly, another study showed that the disease coding for cancer was only 35.7% accurate (Ministry of Health and Welfare, 1996). In comparison, a study of essential high blood pressure insurance claims data in the United States found 74% accuracy (Quamet al., 1993). While this United States study shows that 100% accu-racy is not realistic, the Korean accuaccu-racy is dramatically lower than what could potentially be attained.
In spite of these accuracy problems in the NHIC’s claims data, it is one of the few computerized data sources available in Korea, and it is widely used for national and local disease surveillance, health programme evaluation and policy setting.
STUDY DESIGN
NHIC reviewed all claims in 1996 for five diseases that were not believed to have occurred in the country in that year, and identified 226 organizations that erro-neously submitted claims for these diseases. A coding quality intervention was con-ducted in 1997 by the Ministry of Health and Welfare (MOHW) with all 226 organizations, and follow-up data were collected on coding accuracy in 1998 for these organizations.
The five studied disease codes were selected from 29 legally notifiable commu-nicable diseases: pest, yellow fever, epidemic typhus, diphtheria and polio. Accord-ing to Communicable Disease Prevention Law (CDPL) (MOHW, 1954), physicians are required to report all cases of these diseases to nearby community health centres. We selected these legally notifiable communicable diseases as study subjects for evaluating claims data quality, not only because these diseases have a high level of public awareness so physicians tend to pay more attention in recording and reporting related information, but also because like most other countries Korea has a national surveillance system for these diseases consisting of local community
health centres, which would be used as a vehicle for data quality intervention and evaluation.
There have been no cases of these diseases reported during the study periods from 1 January 1996 to 31 December 1998 according to official statistics (MOHW, 1997– 1999). Furthermore, these diseases are not reported to have occurred for many years: Yellow fever has never been found in South Korea, epidemic typhus has not been reported since 1967, nor diphtheria since 1987, nor polio since 1983 (NIH, 2001). There also appears to be a clinical consensus that these diseases have not been seen in many years (personal communication with infectious disease specialists at St Mary’s Hospital, The Catholic University of Korea). Thus any instances in which diagnosis codes for these diseases appear on NHIC claims can reasonably be assumed to reflect coding errors.
CODING ACCURACY INTERVENTION
In July and August of 1997, MOHW sent official letters and educational guidelines for coding to all 226 health care organizations that in 1996 had submitted claims for the above five erroneously coded diagnoses. In the letter, MOHW notified the orga-nizations of the apparent incorrect diagnosis coding, and requested that coding accu-racy be improved. Although there was no information related to punishment for incorrect coding behaviours either on the letter or in related health laws, some health care organizations may still have felt threatened due to the power and influence of MOHW. Furthermore, the organizations were asked to investigate the reasons why these diseases were coded, and to report the results back to MOHW; local commu-nity health centres and provincial health departments were sent to follow up on these responses. Finally, to assist in data quality improvement, MOHW provided the orga-nizations with manuals for correct diagnosis and coding methods.
In general, interaction within organizations when receiving incorrect diagnosis coding from public health authorities was immediate and positive, which means they just accepted it. Information flow process within them was usually from administrative clerks, to physicians, to administrative clerks again, and finally to nearby community health centres. Though the steps involved in the process could vary to include medical records departments in general and tertiary care hospitals, the process was similar in all hospital types in terms of direction and time of interaction.
Previous work has reported on the investigation of the causes of miscoding, find-ing that errors were due to a combination of physician and clerical errors. The rea-sons for incorrect coding identified through reviewing 2431 cases were mostly from (1) administrative errors by physicians or clerks related to the claiming process (47.0%), for example a physician preferred a certain number when coding regard-less of the real diagnosis, or an administrative personnel claimed with a certain dis-ease code because there was a laboratory test sheet of that disdis-ease on the medical chart regardless of the result; (2) input error of claims data by key punchers of NHIC (31.3%); and (3) diagnosis error by physicians (21.7%) (Shinet al., 1998).
ORGANIZATIONAL CHARACTERISTICS INFLUENCING CHANGE
To better understand the effects of this data quality intervention, we further inves-tigated the relationship between changes in pre/post data errors and the character-istics of the studied health care organizations. The data quality intervention may be viewed as a managerial innovation (Kimberly, 1981) and as such its adoption is a function of the characteristics of the organization. While the study of innovation within health service organizations has identified a range of process and structural characteristics (Hernandezet al., 2000; Mittmanet al., 2002) our analysis was lim-ited to a set of macro characteristics as proxy measures of power and influence of the organization within the larger environment. It is through this influence that the orga-nization is able to mediate its environment and thus either adopt or reject external efforts to influence ongoing processes within the organization.
Data on organizational characteristics were extracted from the National Health Insurance Corporation’s provider database, and matched to the error data. It was hypothesized that the intervention would have a differential impact depending on the characteristics of the organization. Specifically, organizations that have access to key external and internal resources will hold greater power and influence and thus be less responsive to the proposed intervention.
Organizations function within a larger environment and this environment pro-vides the resources necessary for the organization to function and survive (Pfeffer and Salancik, 1978; Thompson, 1967). The level of resource dependency affects managerial choices. The extent to which organizations command greater internal resources, including larger administrative staffs and overall size as a surrogate for slack resources and/or access to a variety of external resources, may give the orga-nization more flexibility in responding to environmental demands (Greening and Gray, 1994). While few studies have explored this relationship within a health ser-vices context, two studies involving the adoption of TQM (Zinnet al., 1998) in nur-sing homes and the study of market characteristics and their impact on nurnur-sing care facility service innovations (Banaszak-Hollet al., 1996) provide some support for the differential impact of environment on organizational functions.
To investigate aspects of these constructs, we measured characteristics which might be considered proxy measures of power and influence within the larger envir-onment including the level of care (clinic, general hospital, tertiary hospital), own-ership (public versus private), service lines (medical, surgical or mixed), location (rural versus urban), average number of employees per department, and the level of equipment technology (proxied by availability of CT and/or MRI). Using these characteristics as a measure of power and influence it was hypothesized that large public tertiary facilities with highly differentiated structures and a high level of tech-nology would be least responsive to the intervention.
STATISTICAL METHODS
Statistical significance of bivariate differences across groups (e.g. public and private) for a given characteristic (e.g. ownership) was assessed for the organization-level mean number of miscoded claims pre-intervention and post-intervention, for the
percent of organizations still exhibiting miscoding post-intervention, and for the per-cent decrease in the number of claims miscoded. Pearson chi-squared tests were used to determine the statistical significance of differences in the percent of organizations with miscoding post-intervention; the remaining variables were tested using least squares-based heteroskedasticity-corrected (Huber) standard errors, as appropriate given the more continuous nature of those variables.
Organization level multivariate analysis was then conducted to further investigate the relationship between the entire set of organizational characteristics, and each of two key dependent variables: the number of miscoded claims submitted by each organization pre-intervention, and the percent decrease in the number of claims reported by each organization [(number of miscoded claims pre-intervention number of miscoded claims post-intervention) / (number of miscoded claims pre-intervention)]. The former variable was modelled using Poisson regression, while the latter was modelled using generalized least squares, weighted by inverse var-iances to correct for heteroskedasticity.
RESULTS
Table 1 shows the number of cases and health care organizations for the five erro-neous disease codes from 1 January to 31 December 1996. Two hundred twenty-six health care organizations submitted 549 cases with these mis-coded claims in 1996. The most frequently claimed cases mis-coded were polio and pest, 142 and 66 cases, respectively. Other mis-coded cases were a little bit over 10 cases each. The average number of mis-coded claims per health care organizations was 2.4, among those that had at least one erroneous claim.
Table 2 shows the number of health care organizations with mis-coded claims and the number of claims before and after the intervention. The total frequency of health care organizations with mis-coded claims decreased from 226 to 13 after the gov-ernmental intervention. Almost all, 94.2%, of health care organizations with mis-coded insurance claims at baseline (1996) changed their coding behaviour so as to have no mis-coded claims in the year following the intervention (1998). The per-centage change by level of care was 98.6% for clinics, 92.6% for hospitals, general hospitals, and 68.4% for tertiary care hospitals.
Table 2 also shows results of the bivariate analysis of organizational factors related to coding changes by health care organizations. Results were similar across Table 1. Disease codes analysed for insurance claims accuracy in 1996
Disease ICD-10 codes Claims Health care organizations
Pest A200A209 116 66
Diphtheria A360A362 91 12
Epidemic typhus A750 39 12
Polio A800A809 274 142
Yellow fever A950A959 29 13
Total 549 226
two related dependent variables: the proportion of organizations still with any mis-coding post-intervention, and the percentage decrease in the number of miscoded claims. Statistically significant organizational characteristics which were related to greater improvements at¼0.05 were low level of care (more prone to change in clinics than tertiary care hospitals), private ownership (private), less complex mix of service lines, lower resource intensity (employee/department ratio) and less avail-ability of high technology equipment.
The multivariate analysis (Table 3) investigated which of these factors remained important after controlling for the others. In analysing the number of miscoded claims pre-intervention, the correlates of higher claims that remain significant are having a medical service line only, rural location, and higher resource intensity (employees/department). Of more interest for analysing the intervention is the multi-variate regression analysis of the percent decrease in claims post-intervention, which revealed that those facilities with significantly smaller percent improvements were Table 2. Change in miscoding rates, by organizational characteristics
Pre-intervention Post-intervention Characteristic Number of Mean Mean Organizations Mean
organizations miscoded miscoded with decrease claims claims miscoding of claims
(%) miscoded (%) Level of care Clinic 139 2.19 0.02a 1.44b 99.1b Hospital/General hospital 68 2.32 0.09 7.35 92.9 Tertiary hospital 19 4.58 0.58 31.58 90.2 Ownership Private 211 2.47 0.08 4.76b 97.0 Public 15 1.80 0.20 20.00 88.9
Major service line
Surgical service 41 1.17c 0.00b 0.00c 100.0b Medical service 100 2.60 0.03 2.00 98.7 Mixed 62 3.27 0.27 17.74 89.2 Location Rural 25 4.88 0.00 0.00 100.0 Urban 201 2.12 0.10 6.47 96.0 Employees/Department 1–5 60 1.37b 0.00b 0.00c 100.0b 6–30 43 2.16 0.12 6.98 92.4 >30 36 4.28 0.39 25.00 89.7 Equipment availability Low 118 2.17 0.00b 0.00c 100.0b Moderate (CT or MRI) 26 2.38 0.12 7.69 97.0
Excellent (CT and MRI) 24 3.91 0.36 22.73 85.9
Total 226 2.43 0.09 5.75 96.5
Statistically significant rejection ata10%,b5%, andc1% levels of null hypothesis that column mean or proportion is equal across groups for a given characteristic. Tests for equivalence of means are based on heteroskedasticity-corrected linear regression standard errors, and tests for equivalence of proportions use Pearson chi-squared statistics.
those that were public and had more high technology equipment. The lack of signifi-cant results for other characteristics such as level of care, however, should not be over-interpreted given that many of these characteristics are highly collinear. For example, tertiary hospitals were all urban, and had complex mixed service lines with large departments and high technology. While as a result it was not possible to pre-cisely assess the independent effects of each attribute controlling for all of the others, there was nevertheless a clear pattern of larger and more influential organizations being less successful at responding to the intervention to reduce coding errors.
DISCUSSION AND CONCLUSIONS
In general, health care organizations appeared responsive to this governmental claims data quality intervention, with overall 94% eliminating all mis-codings in Table 3. Multivariate analysis of organizational determinants of miscoding
Pre-intervention Post-intervention
Characteristic Number of % decrease in
miscoded claims claims miscoded Level of care (Clinic) — — Hospital/General hospital 0.40 (0.82) 0.06 (0.44) Tertiary hospital 0.25 (1.05) 0.05 (0.45) Ownership (Private) — Public 0.36 (0.34) 1.58c(0.22)
Major service line
(Surgical service) — — Medical service 1.06b(0.46) 0.18 (0.11) Mixed 0.84 (0.82) 0.74 (0.45) Missing 0.68 (0.80) 0.67 (0.45) Location (Rural) — — Urban 1.26b(0.63) 0.04 (0.12) Employees/Department (1–5) — — 6–30 0.60b(0.28) 0.04 (0.25) >30 1.39b(0.70) 0.17 (0.27) Missing 0.85a(0.48) 0.29 (0.26) Equipment availability (Low) — — Moderate (CT or MRI) 0.59 (0.55) 0.03 (0.30)
Excellent (CT and MRI) 0.69 (0.66) 0.97c(0.32)
Missing 0.53 (0.48) 0.12 (0.21)
R2 0.17 0.74
Number of observations 226 226
Statistically significant at thea10%,b5%, andc1% level. Poisson regression coefficients are reported for the model of the number of miscoded claims (pre-intervention). Hetero-skedasticity-corrected weighted least squares coefficients are reported for the model of the % decrease in claims miscoded.
the post period. To the extent that these results are causal, the dramatic change could be a response to potential financial losses, since income from the national health insurance claims comprise the vast majority of the organizations’ income.
The investigation of organizational characteristics revealed that large tertiary facilities with more intense resources, complicated processes of service provision, and perhaps more political power were less responsive to the intervention. Among these characteristics, the level of care provided the widest range of responses with tertiary hospitals being the least responsive group. This is despite the fact that ter-tiary hospitals would benefit most from economies of scale when investing in infor-mation system quality upgrades.
Further analysis revealed that of the three public organizations that did not elim-inate all errors, all three were national tertiary care hospitals. Since these are large and very influential organizations their failure to respond to the intervention support the underlying hypothesis that such organizations can maintain their autonomy and remain relatively independent of external influence (Shortell and Kaluzny, 2000).
It is not possible to determine conclusively from a demonstration project such as this whether the organizations truly improved their ability to code accurately, or whether they simply learned to avoid submitting claims with these five disease codes. At the very least, however, it is striking to observe the increased attention to avoid coding the studied diseases, across a wide range of different health care organizations. The results point to the potential for large data quality improvements from wider adoption of such interventions, at least among selected facilities, which could have considerable long-term benefits for health care systems. Within those hospitals that were not responsive to the intervention, attention needs to be given to the identification of a more tractable set of variables that are amenable to manage-rial interventions that would facilitate the adoption of the data quality intervention but also to simply improve coding processes and other work process activities within the organization. These characteristics include the prevailing culture of the organization, the supporting infra structure as well as training opportunities all of which are amenable to improved management practices that would facilitate the adoption of the data quality intervention as well as improve coding processes as part of normal work flow activities.
ACKNOWLEDGEMENTS
The authors thank the National Health Insurance Corporation for the data and the Ministry of Health and Welfare, Community Health Centers and Provincial Health Departments for their support of the study.
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