Impact of Over coding and Under coding on Health Indicators
HIM 4103 – Health Data Analysis
Assessment 2: Health Data Analysis Applications in Performance Indicators and Healthcare Funding
In our classes we have discussed methods used in health care to analyse a variety of performance
indicators in management. You may access one of the following health websites from the UAE and
discuss the items that follow.
DHA:
http://www.dha.gov.ae/En/sectorsdirectorates/directorates/healthregulation/healthcareinvestment/pa
ges/statisticalreports.aspx
SEHA:
http://www.seha.ae/SEHA/Annual%20Report/AR%202012/pdf/seha_ar12_en.pdf
HAAD:
http://www.haad.ae/HAAD/LinkClick.aspx?fileticket=JY0sMXQXrOU%3d&tabid=349
Discuss the impact of over-coding and under-coding on healthcare funding.
Select at least 3 performance indicators (use examples from the list below or select your own
performance indicators). Identify how over- and under-coding can impact the 3 indicators.
Monitoring LOS
Normal pathology results
Medication error
Unnecessary diagnostic tests
Unnecessary medication
Next, your report should include discussion of the following items:
Application of Diagnosis Related Groups (DRG’s) and Cost Weight methodology.
Impact of incomplete documentation on the health record
How your selected performance indicator can be used to manage healthcare funding and one of
the following:
o Quality management
o Utilization management
o Risk management
o HR management
Recommendations
Conclusion
Solution
Introduction
In medical terms, the process of coding involves the transformation of diagnoses and
healthcare procedures into an alphanumeric series of codes, notes Smiley (2015).Coding is an
integral part of Electronic Health Records. Soteriades et al (2013) note that “the use of EHR
offers instant access to medical information,” and may help save lives during emergencies by
speeding up diagnoses.The codes derived for diagnoses and the healthcare procedures are
extracted from the medical records including the results of laboratory tests, notes from attending
physicians, diagnosis summaries and results from radiology tests.
The process of coding is necessary to help reduce the amount of information availed to
medical insurance companies. It also helps in the storage of patient information by helping
reduce the bulk that such information would ordinarily have.Coding is also important for billing
and the provision of healthcare funding; Smiley (2015) notes that “medical billers have to
interact with both patients and insurance companies in order to ensure claims are paid within
reasonable time.” This creates a need for alpha-numeric representation of patient data in order to
reduce the information load and reduce the time lag between claim and reimbursement.
Under Coding&Over Coding in Health Data Analysis
The above problems refer to the two errors that occur during the conversion of patients’
analog information into an alpha-numeric form for storage and ease of management.
Under Coding
This refers to a situation where the code that medical billers in a healthcare facility bill
does not represent the full extent of the information that it should. In many instances, it does not
include much of the work done by physicians, leading to lower than required revenue allocation
towards healthcare processes. With Undercoding, Smiley (2015) notes, “some procedures may
HEALTH DATA ANALYSIS 4
be left out and hence the resultant code represents a less serious procedure than was actually
carried out on a given patient.” Due to the error in coding, the health care provider earns less for
medical procedures that, would in reality, cost more. As a result, the medical service provider
makes losses.
Over Coding
Different from under coding, the process of over coding occurs in scenarios where the
alpha-numeric representation created by medical billers reports a higher code than the process
that was actually carried out by physicians. The error of overcoding, as a result, leads to the
allocation of higher revenue than should be received. Just like the error of under coding, the
process of overcoding is detrimental since it may lead to misdiagnosis of a patient because of the
addition of unnecessary information in such a patient’s medical record.
Impacts of Overcoding & Undercoding in Health Care Funding
Overcoding always leads to higher codes than the processes that are actually carried out.
This leads to the levying of unnecessarily higher charges levied upon medical care providers by
the medical billers in hospitals. This results in the insurance companies in question paying higher
amounts in hospital and medical bills than should be the case (Srinivasan & Arunasalam, 2013).
A case in point, notes Rudman, Eberhardt, Pierce & Hart-Hester (2009), was the Florida example
in which a dermatologist was sentenced to 22 years in prison, paid $3.7 million in
restitution,forfeited an addition $3.7 million, and paid a $25,000 fine for performing 3,086
medically unnecessary surgical procedures on 865 Medicare beneficiaries. All these costs had
been charged as a result of overcoding.
Holders of insurance policies may be forced to pay higher premiums due to the error of
overcoding which tends to include information on conditions that the policy holder may not
HEALTH DATA ANALYSIS 5
necessarily have. As a result of the exaggeration of a holder’s medical condition, insurance
companies may be forced to readjust the cost of premiums in order to fit the sum of the policy.
This usually tends to cause a strain on the finances of the affected individual leading to financial
misery.The affected parties may be forced to readjust their lifestyles in order to keep up with the
medical insurance premiums that result from the error of overcoding (Burns et al, 2012).
Overcharging which results from the overcoding by medical billers tends to cause a
disproportionate allocation of Medical aid funds to cases that do not necessarily need to receive
the much they do. This leads to a shortage of funds among authentic cases that should receive
such funds. Rudman, Eberhardt, Pierce & Hart-Hester (2009) write of the Raritan Bay Medical
Center case in which the healthcare provider agreed to pay the government $7.5 million to settle
allegations that itdefrauded the Medicare program over a period of time. The medical centre had
been purposely inflating charges for inpatient and outpatient care thereby artificiallyobtaining
outlier payments from Medicare through overcoding of patient information. Such a scenario
leads to inadequate funds in the program and hence a shortage of care in more deserving cases.
Redman, Eberhardt, Pierce & Hart-Hester (2009) claim that it is projected that such errors
account for the loss of between 3 to 15 percent of annualexpenditures for healthcare in the
United States.
Overcoding causes the performance of unnecessary procedures among patients. Due to
errors encountered in the process of shifting patient information from an analog to an alpha-
numeric format, over coding may lead to addition of information that should not be included in a
patient’s file. As a result of this, follow up visits may lead to the performance of unnecessary
medical procedures that would further strain the Medicare program. The case of the Florida
HEALTH DATA ANALYSIS 6
dermatologist who performed unnecessary medical surgery procedures on 865 Medicare
beneficiaries is a good example of this.
On the other hand, undercoding may lead to the understatement of charges to be levied
upon patients. This leads to lower costs being paid by insurance companies for processes that
would ordinarily cost more than they are charged. This leads to the affected medical services
provider incurring more costs in its daily operations than the revenues forthcoming from those
operations. As a result, such medical care facilities tend to incur losses. The losses incurred by
healthcare service providers may lead to a reduction in staff due to layoffs in line with cost
cutting measures, leading to a reduction in the quality of service offered in such facilities. In the
event of such losses being sustained over a long period of time due to undercoding errors, the
medical service provider in question may be forced to close down its operations; causing a
further strain on the already inadequately spread medical facilities, and causing a reduction in the
quality of healthcare services, claims Buck (2015).
These problems of coding have increased the cost healthcare in the United States from an
average of US $ 4,316 in 2010 to the 2015 figure of US $ 6,566. This means that the cost borne
by individual has increased from 10% to 14% in just five years. These are shown in Appendix 1.
Impact of Overcoding & Undercoding on Certain Health Indicators
i. Medication Error
The error of overcoding is detrimental to medication in the offering of healthcare
services.This is because the error leads to the inclusion of information that does not match a
patient’s condition. This leads to a misdiagnosis and could lead to the prescription of wrong
medication to an individual.This may ultimately lead to malpractice on the part of the presiding
medical practitioner.
HEALTH DATA ANALYSIS 7
Overcoding may also lead to an over prescription of medication for conditions that may
have been recorded as more serious than they actually are. This leads to the use of more potent
treatment solutions for conditions that only require mild solutions. A scenario like this would
involve patients being subjected to medication methods such as surgeries while their ailments
may require less invasive treatments like oral or introviral medication
Undercoding of patient data by the responsible parties leads to the information entered
into health care provider’s record systems for a certain patient being inadequate. This may lead
to the omission of critical life-threatening information in the process of diagnosing patients or
recommending treatments for such patients. This may cause an inadequacy in the medication
recommended for the patient whose information is under coded.
The error of undercoding may lead to an error in the prescription of drugs for conditions
that may require more attention than is actually accorded to them. This may result from the
recording of serious conditions as mild situations leading to the casual handling of serious
medical issues due to understatement of facts and information regarding a given patient.
ii. Unnecessary Diagnostic Tests.
In the event of a medical emergency, where the attending physician only has access to the
patient’s medical history, the doctor in question will usually only rely on the information
provided in the records. If the information had been overcoded, the doctor will base the medical
decisions made upon such record. This may lead to unnecessary diagnostic tests especially in
situations where the symptoms observed by the doctor do not match the records provided in the
patient’s medical history.
Undercoding may lead doctors to carry out unnecessary diagnostic tests that would have
been easily avoided with the act of correct coding. This is because undercoding involves the
HEALTH DATA ANALYSIS 8
omission of pertinent information that is vital in the records of a patient, leading to a lower code.
Subsequent treatments may be based upon such lower codes if no additional tests are carried out;
however, if the attending physician notices disparity between the symptoms and the records, they
may recommend diagnostic tests, that tend to be invasive to patients, but would have been
avoided if only correct coding had been done earlier on.
iii. Normal Pathology Results
Through overcoding, the results of normal pathological tests may be exaggerated, leading
to a wrongful diagnosis. This would then lead to an overmedication of a patient whose ailments
would have required much simpler methods of treatment. The exaggeration of normal pathology
results may also lead to the redirection of hospital resources, in terms of equipment and
manpower, to the treatment of a case that would ordinarily require fewer resources; leading to a
poor prioritization of such resources, thus denying genuinely deserving cases (Rudman,
Eberhardt, Pierce & Hart-Hester, 2009).
Undercoding may lead doctors to interpret serious conditions as normal ailments that do
not require any special attention, much to the detriment of a patient’s health. In instances where
the undercoded illness happens to be contagious, the error in coding would lead to an epidemic
due to the poor representation of information regarding an illness that would have been contained
better if the right priority had been accorded. As a result of undercoding, a simple case of a
contagious disease may turn out into an epidemic, creating a pressure, which would have been
easily avoided, on resources that would have been better utilized in the service of other patients.
Impact of Incomplete Documentation on Health Records
Incomplete documentation of health records is very risky. For one, it may lead to a
misstatement of facts regarding a patient’s condition. This may lead to the use of wrong methods
HEALTH DATA ANALYSIS 9
of treatment on the patient in question. For instance, a patient may be subjected to more potent
treatments that are not necessary for their illness due to poor documentation (Mathaeur &
Wittenbecher, 2012). On the flip side, a patient may be subjected to mild treatment for an illness
that requires more aggressive methods, leading to risks being posed to the patient. For example,
an understatement of cancer due to incomplete documentation of health records may lead to a
patient being subjected to surgery while chemotherapy would have just sufficed.
Application of Diagnosis Related Groups (DRG’s) and Cost Weight Methodology
The DRG methodology, according to Mathauer & Wittenbecher (2012) is a statistical
method of diagnosis, designed for grouping patients who are beneficiaries of Medicare for
purposes of payment. The method divides the possible diagnoses into 20 groups based on the
major parts of the body. These groups are further divided into 500 groups for reimbursement
purposes. According to Allexander (2011), the system is linked to a fixed payment amount based
on such factors as diagnosis, hospital resources used, age of patient, and the procedure used. The
DRG method is best for use in instances involving a large group of patients under a giant
healthcare insurance coverage provider, much like the situation with Medicare whose number of
beneficiaries, over the years, is listed in Appendix 2.
The Cost Weight methodology involves the categorization of patient costs according to
weight. In this method Mathaeur & Wittenbecher (2012) note that patients are categorized based
on the cost of their treatment for purposes of reimbursement by health insurers and schemes such
as Medicare. The cost of each service is allocated based on the hospital resources used and the
procedures used. This method is best applicable in instances where the number of patients is
small because it involves more information which would be too bulky in a scenario involving
many patients.
HEALTH DATA ANALYSIS 10
Use of Cost Weight Methodology in Quality Management
The cost weight methodology involves allocation of costs of healthcare based on the
hospital resources and procedures used in the treatment of patients. This method only allocates
costs where actual treatment occurs. It is therefore important in the management of treatment
processes and patient care in hospitals. As such, it is a great methodology for quality
management and control in the healthcare industry especially where the system works on
reimbursements, as is the case with Medicare.
Recommendations
In light of all the methodologies discussed in this paper, it is better to incorporate the use
of the cost weighted method in a situation where the patients treated belong to a scheme covering
only a small number of people. In instances of mass coverage providers such as Medicare, it
would be unwise to use the Cost weighted method; therefore, the DRG method would serve
better in such an instance.
Conclusion
Both errors of under coding and over coding are detrimental to the provision of
healthcare services. Overcoding leads to a number of legal issues such as negligence, fraud, and
misrepresentation of facts. On the other hand, undercoding leads to losses for health services
providers and in effect causes a strain on already insufficient resources. In order to avoid these
errors and maintain quality in the offer of healthcare services, it is important to adopt a mixture
of both DRG and Cost Weighted methodologies.
HEALTH DATA ANALYSIS 11
References
Allexander, B.D. et al., 2011. Medicare.Fundamentals of Health Law, 81(1), 111.American
Health Lawyers Association.
Buck, C.J. (2015). Step-by-Step Medical Coding.Elsevier Health Sciences.
Burns, E.M., Rigby, E., Mamidanna, R., Bottle, A., Aylin, P., Ziprin, P., & Faiz, O.D.
(2012).Systematic Review of Discharge Coding Accuracy.Journal of Public Health,
34(1), 138 -148.
CMS (2016).On Its 50 th Anniversary, More than 55 Million Americans Covered by
Medicare.www.cms.gov/Newsroom/MediaReleaseDatabase/Press-releases/2015-Press-
releases-items/2015-07-28.html
Mathaeur, I., & Wittenbecher, F., 2012. Hospital Payment Systems Based on Diagnosis-Related
Groups: Experiences in Low and Middle-Income Countries. Bulletin of the World Health
Organization, 91(1), 746 – 756. World Health Organization.
Rudman, W.J., Eberhardt III, J.S., Pierce, W., & Hart-Hester, S. (2009). Healthcare Fraud and
Abuse.Perspectives in Health Information Management 6(1), 1 – 25.
Smiley, K. (2015).Medical Billing & Coding for Dummies.John Wiley & Sons.
Soteriades, H.G., Neokleous, K., Tsouloupas, G., Jossif, A.P., & Schizas, C.N. (2013). Electronic
Health Record: Facilitating the Coding Process. 13 th IEEE International Conference on
BioInformatics and BioEngineering (pp. 1 – 4). IEEE
Srinivasan, U., & Arunasalam, B. (2013).Leveraging Big Data Analytics to Reduce Healthcare
Costs.IT Professional, 15(6), 21 – 28.
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