While that debate still rages, the rubber is about to meet the road. Technically, physicians should have been documenting when most of those conditions were present on admission for the past year, as a way to report the incidence of these complications. But that reporting function will now translate into real dollars. While physician fees will not be affected, the payments that hospitals will receive for treating these conditions certainly will. And the CMS recently announced that three more conditions are being added to that list: certain clinical manifestations of poor glycemic control; deep-vein thrombosis or pulmonary embolism following total knee and hip replacement; and surgical site infections following certain elective procedures, including some orthopedic surgeries and bariatric surgery.
How can you be sure your coding and documentation support a present on admission status? What is POA? Present on admission is defined as a condition that is present at the time the order for inpatient admission occurs. As far as documentation, work with your hospital staff and coders to determine the best way to communicate POA information.
Be sure to make that notation for any of these conditions, whether or not they are the primary or secondary diagnosis. The importance of documentation Clearly, the ability to use any of these indicators depends on how well you document.
Only complete and accurate documentation will allow coders to select the appropriate indicator to use when billing Medicare. Fortunately, coders will be able to consider all physician documentation relevant to an admission when choosing an indicator. The CMS has suggested that the discharging physician clearly indicate in the discharge summary which of the conditions were or were not present on admission. Again, simply documenting POA next to the diagnoses in question should make that clear.
At the same time, expect plenty of confusion. It flared up and he took care of it. Some classroom time would help, Cunningham says. But if they understand how this impacts reimbursement and the quality of care of their patients, then they should be able to understand what they are supposed to contribute to this process.
Still, coding departments should not be so quick to pass the buck. Wozniak says the ability to accurately and efficiently document and code for POAs and HACs also can depend on whether an organization has the resources to implement a clinical documentation improvement CDI program.
In the end, Cunningham says physicians, coders, and CDI specialists must collaborate effectively to produce accurate, concise, and clear documentation that will enable coders to code the record and defend and support that coding. Accuracy rates will likely vary by case, Wozniak says, "With certain diagnoses, the accuracy rate is always high because of the nature of the condition. You can't develop cancer while you're in the hospital—that's just impossible—so you'll always have a Y on them," she says.
For example, you can't have an immediate complication of a caesarian section anywhere but in a hospital. So some of them are very clear and are going to be very accurate. Of the cases reported inaccurately, the highest percentages were attributable to stage III and stage IV pressure ulcers. In FY , hospitals were subject to payment reductions. Across the and programs, the average performance on two of three measures included in both years improved, while one—CAUTI—worsened.
But if the physician doesn't link those, the coders can't code it to catheter-associated UTI. None of the hospitals shifted more than 1 quintile. Mean differences in hospital-level risk-standardized readmission rates between models using the CoC algorithm vs POA were very small across all 3 readmission measures eTables in the Supplement.
Only a small proportion of hospitals in the AMI and PN mortality measures shifted 2 quintiles; no hospitals shifted 2 quintiles in the HF mortality measure. None of the hospitals shifted more than 2 quintiles. Mean differences in hospital-level risk-standardized mortality rates between models with and without POA indicators were small across all 3 mortality measures, albeit larger than for the readmission measures eTables in the Supplement.
Model performance improvements were more substantial for the mortality measures compared with the readmission measures. As expected, with the incorporation of POA indicators, we observed an increase in the number of comorbidity risk variables per beneficiary on an index admission.
By incorporating POA indicators, many comorbidities that are currently excluded from risk adjustment in CMS outcome measures because they cannot be determined to be complications or comorbidities can be identified as present upon admission and therefore included in the risk models. This allows for improvement in model discrimination and greater face validity of the risk models.
All patient-level models showed either no change or slight improvements in goodness of fit, suggesting that models that incorporate POA indicators at the index admission may better estimate the risk of readmission and mortality compared with current models that only use the CoC algorithm.
There were minimal changes in hospital-level quintile shifts for the 3 readmission measures, but more variation in hospital-level performance was present across the 3 mortality measures, highlighting that mortality may be more influenced by patient-level clinical factors. We did not assess the validity of POA indicators; however, existing literature suggests that POA reporting is relatively consistent with medical record information.
Other research has investigated the potential impact of POA on hospital rankings, as shifts in rankings could be a factor in hospital reimbursement if the outcome measures are included in CMS payment programs. As noted previously, most of the studies reviewed were conducted at the health system or state level and focused on outcomes for specific diseases or procedures, limiting the generalizability of the findings.
The findings of this study are consistent with prior research on how POA indicators factor into risk models, 3 , 4 , 16 although, to the best of our knowledge, this is the first study to show such evidence in risk models defined by ICD codes.
One previous comparative effectiveness study on similar mortality cohorts 3 showed improvements in risk models that included POA indicators in the ICD-9 coding system. A subsequent study of select CMS payment measures similarly demonstrated an incremental benefit. The observed improvements in model performance for measures that are already in public reporting are promising. Furthermore, model improvements were consistent across the 6 outcome measures tested for this study, and analyses encompassed all Medicare FFS beneficiaries aged 65 years and older nationally.
Our study has several limitations. We did not assess the accuracy of POA coding by hospitals in this study. Previous research has suggested that the use of POA to account for hospital case mix may be prone to gaming if POA indicators are incorporated into measures that impact payment. The relatively small model enhancements seen with POA suggest that current measure methodology that does not use POA status is still a valid option for hospitals, such as CAHs, with lower POA reporting likely due to differential reporting requirements.
Published: May 12, Corresponding Author: Elizabeth W. Author Contributions : Dr Li and Mrs Xin had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. No other disclosures were reported. However, the funder reviewed the manuscript and approved it for publication.
The content of this article does not necessarily reflect the views or policies of the US Department of Health and Human Services nor does the mention of trade names, commercial products, or organizations imply endorsement by the US government.
The authors assume full responsibility for the accuracy and completeness of the ideas presented. Our website uses cookies to enhance your experience.
By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy Continue. Download PDF Comment. View Large Download. Table 1. Table 2. Table 3. Table 4. Note on Quintile Shifts in eTables eTable 1. Updated February 11, Accessed March 10, HCUP methods series report September 1, Comparative effectiveness of new approaches to improve mortality risk models from Medicare claims data.
QualityNet website. National patterns of risk-standardized mortality and readmission for acute myocardial infarction and heart failure: update on publicly reported outcomes measures based on the release. Created March Accessed August 23, Hospital Compare website. Accessed March 1, Predictive value of the present-on-admission indicator for hospital-acquired venous thromboembolism. The accuracy of present-on-admission reporting in administrative data.
Effect of present-on-admission POA reporting accuracy on hospital performance assessments using risk-adjusted mortality. Impact of present-on-admission indicators on risk-adjusted hospital mortality measurement.
0コメント