Excite welcomes you to the second in a three-part series focusing on the uses and effects of coded data on three different levels: use by private agencies, healthcare providers, and the individual patient. In the first article, we discussed data as it pertains to private agencies such as the CDC and the Agency for Healthcare Research and Quality (AHRQ). The data is blinded, compiled, and used for research and quality monitoring purposes on state and federal levels.
One recent example of this is the new ICD-10-CM codes G40.83X, effective October 1, 2020, for Dravet syndrome, a neurodevelopmental disorder. The designation of the ICD-10 codes resulted from efforts by specialists in the field of Dravet syndrome.
Dedicated ICD-10 codes for Dravet syndrome will make it easier for the field to conduct epidemiologic research and studies, determine morbidity and mortality rates, track outcomes of clinical interventions, and develop protocols for the standard of care. Prior to the specific codes, Dravet syndrome was included in the ICD-10 code G40.8 Other epilepsy and recurrent seizures, which contained a broad group of epileptic disorders with different causes and treatment strategies that are not specific to Dravet syndrome.
In this article, we will discuss uses on a more local level – the healthcare provider at the physician level or organization level. Three key areas we will explore involve:
Coded data plays an essential part in each.
The goal for every claim should be to reflect the patient’s true clinical picture, to tell the clinical story of the patient’s encounter, and to do so within the coding guidelines for the specific encounter type (for example inpatient, versus outpatient, versus professional fee coding guidelines). In accurately reflecting the patient’s clinical picture and the encounter or visit through coding, this sets up the healthcare provider for accurate reimbursement.
On an inpatient claim, if the reported DRG is not the most appropriate DRG for that patient’s clinical picture and inpatient stay, the cause is usually poor documentation, improper coding, or a combination of both. Results can include over or under reimbursement and an inaccurate reflection of the severity of illness and risk of mortality.
Overpayment increases government and payer scrutiny and could result in paybacks and even fines plus continued monitoring by the payer.
Underpayments can result in a low case mix index and cause financial hardships for the healthcare provider. Also, under-reporting of patient complexity can result in poor comparison studies.
Let’s take a look at an example with differences that impact reimbursement. A patient is admitted and treated for pneumonia, acute on chronic combined congestive heart failure (CHF), and acute kidney failure.
|Principal DX: Pneumonia||Principal DX: Pneumonia||Principal DX: Pneumonia|
|Secondary: Heart failure (not documented as acute/chronic, systolic or diastolic)||Secondary: Heart failure (not documented as acute/chronic, systolic or diastolic)||Secondary: Acute on chronic systolic and diastolic heart failure (MCC)|
|Secondary: Renal insufficiency (not documented as acute kidney injury)||Secondary: Acute kidney injury (CC)||Secondary: Acute kidney injury (CC)|
|195 Simple Pneumonia and pleurisy without CC/MCC||DRG 194 Simple Pneumonia and pleurisy with CC||DRG 193 Simple Pneumonia and pleurisy with MCC|
|RW 0.6650||RW 0.8630||RW 1.310|
|Reimburse based on base rate of $4000 = $2,660.00||Reimburse based on base rate of $4000 = $3,452.00||Reimburse based on base rate of $4000 = $5,240.00|
The table above demonstrates that incomplete documentation or coding can greatly affect the case mix and reimbursement. If this is extrapolated out over a number of charts for a single facility, the negative impact is amplified.
A low case mix index typically infers a lower acuity of patients being treated. Facilities may be treating patients who are very sick, but because of documentation and subsequent code assignment, it will not be reflected as such.
Quality of care research
Quality and outcomes can be reflected in ICD-10-CM and ICD-10-PCS codes and other data points that are abstracted during the admission and immediately following discharge such as complications, co-morbidities, discharge disposition status, and present on admission indicators. Below we explore a few examples of coded data impacting the quality of care research.
The Hospital Readmissions Reduction Program (HRRP) penalizes hospitals by withholding up to 3% of regular reimbursements if they have a higher than expected number of readmissions within 30 days of discharge for six specific conditions. Certain coded conditions or situations can reduce the penalty, such as patient non-compliance, Z91.1X.
Social determinants health (SDOH) – such as homelessness, employment, ability to physically get to a healthcare provider for care– are critical data points that could impact patient outcomes. Capturing SDOH data can enable health care providers to know when to incorporate plans into care management work processes to provide actionable data at the point of care, improve discharge planning, and reduce the risk for readmissions and improve patient outcomes.
Coded data can be critical in analyzing risk factors that impede positive outcomes, allowing for the creation, development, and implementation of quality of care programs. A community studied their data on the External Causes of injuries. A high volume of head injuries in youths was related to skateboarding accidents. A community program was created on skateboarding safety as well as a law implemented requiring helmets for those under 18. As a result, the hospital had fewer head injuries to youths due to skateboarding accidents. These efforts were supported through the studies of ICD-10-CM coded data.
Accurate data is critical to appropriate planning. Inaccurate data could have serious consequences. Post-operative complication rates are critical data points for all surgeons and hospitals. In an example of poor coded data, a surgeon documented postoperative ileus on a routine basis on his patients following GI surgery. The hospital coders reported a GI post-operative complication code sending the surgeon’s surgical complication rate through the roof. The rates were published in the local newspaper resulting in a negative impact on the surgeon’s professional reputation and a loss of patients for the surgeon and the hospital. The reality is that the patients had an expected absence of bowel sounds following a GI procedure and the surgeon wrote post-operative ileus instead of the absence of bowel sounds. All of the post-operative complication codes were incorrect. Even after correcting the coding, the damage to the surgeon’s reputation was done.
Comparisons studies and marketing efforts
A complete and accurate coded database will reflect the facility population’s severity-of-illness and will support the length of stay, resource consumption, and decision to admit versus treat as an outpatient. This in turn will promote appropriate facility profiling and scorecards.
Inaccurate data can have a negative impact on physician profiling and marketing. Patients can choose their healthcare provider. A positive post-operative complication rate is critical in instilling patient confidence. As detailed above, a negative postoperative complication rate, whether accurate or not, can cause a loss of patients to other healthcare providers.
Various groups offer comparative data open to the public. Healthgrades and Leapfrog are two examples. Healthgrades evaluates hospital quality for conditions and procedures based solely on clinical outcomes to help consumers understand and compare hospital performance to support their care choices. Anyone can use their website to research the best-ranked healthcare provider in their area.
Coded data is also used to reflect the patient population severity of illness during some managed care contract negotiations.
It is critical to accurately reflect an organization’s true clinical severity beyond the DRG. All secondary conditions are as critical to report as the accurate DRG.
For comparative purposes, Hospital A admits Patient A. Hospital B admits Patient B. Both patients have the same assigned DRG with the same relative weight. Hospital A’s patient was inhouse for 8 days with a cost of $18,000. Hospital B’s patient was inhouse for 3 days with a cost of $6,800. The secondary diagnoses explain the difference. Hospital A’s patient had a much higher degree of patient severity of illness with multiple complicated conditions while Hospital B’s patient had minor complicating secondary conditions. All it takes is one CC or MCC to assign the highest DRG. But when patients have multiple complicating conditions, this can increase the LOS and resource consumption. Secondary diagnosis codes can paint the full clinical patient picture explaining and supporting higher LOS and costs.
Many facilities have implemented measures to identify areas of improvement by performing regular physician education and feedback, maintaining a strong clinical documentation improvement program, and investing in continuing quality monitoring and education of coders. Through continuous improvement efforts on all fronts, an organization can improve clinical documentation, which in turn improves coding, reimbursement, and optimal case mix.
Lisa Marks, RHIT, CCS – VP of HIM Services
Robyn McCoart, RHIT – Managing Auditor