The New York Times recently reported on allegations that a hospital chain was unnecessarily admitting patients from the emergency department (ED) to increase revenues from Medicare and Medicaid. According to the article, one hospital was accused of maintaining a scorecard to keep track of physicians who did not meet a target of admitting at least 50% of ED patients over age 65.
Healthcare reform is creating new financial challenges for many hospitals and there is increasing pressure to find new ways to generate revenue. Certainly, most organizations are not planning to implement illicit programs to meet revenue targets, but these types of stories highlight the need for health plans, employers, and all payers of healthcare to monitor utilization patterns even more closely.
Admission rates from the ED can easily be monitored in MedInsight. A few illustrative examples from a demonstration dataset based on an elderly population are presented below.
Table 1 displays the ED visit rates, percent of ED visits admitted and admission rates across four regions. Region 3 has the highest ED visit rate, the highest percent of ED patient visits admitted, and the highest overall admission rate. These high level statistics do not indicate that any of the ED visits, or resulting admissions are inappropriate, but the findings suggest a reason to evaluate ED admission patterns in Region 3 more closely.
Table 1: ED Visit Rates and Admission Rates by Region
Table 2 displays the number of ED visits and admissions percentage of ED visits admitted for the top 9 hospitals (in terms of ED visits) for Region 3.
Hospital 1 has the highest number of ED visits and the highest percent of ED patients admitted into the hospital. Thirty-nine percent of all ED patients were admitted.
Table 2: ED Visits and Admissions by Top Hospitals
Table 3 displays the ED visits at Hospital 1 by diagnostic category and the percent of visits within each category that were admitted to the hospital. Sixty-five percent of the visits for digestive conditions and 59% of visits for circulatory disorders were admitted to the hospital.
Table 3: Hospital 1 Admissions by ED Visit Diagnosis Category
 Abelson, R. & Creswell, J. (2014. January 23). Hospital Chain Said to Scheme to Inflate Bills. The New York Times, retrieved from www.nytimes.com.
Over the last few years one day hospital stays have been a focal point for medical management efforts to convert these to observation stays. In addition the 2014 Medicare’s Inpatient Prospective Payment System’s final rule on inpatient admission defines an “appropriate” inpatient admission as one that in the judgment of the admitting physician requires a hospital stay of at least two midnights or in medical management terms, a two day length of stay (LOS). Patients not meeting this criteria but needing inpatient hospital care lasting past one midnight but less than two will be classified as observation cases. The combination of these and other factors is leading to an increase in utilization for observation. So is this a positive outcome from a cost management perspective or is it problematic? This is becoming an important question for population management and one that does not have a simple answer.
A not uncommon situation is one where the reimbursement levels for inpatient hospital admissions and observation stays are not rationale. By this I mean that observations stays are paid more than if the patient were admitted as a regular inpatient admission. Avoiding an admission through the use of observation may be the right thing to do in this situation from a resource efficiency perspective but may actually cost a payer more than the hospital admission that is being avoided. Regardless, the overall trend is to move patients to observation status when this is appropriate clinically.
So how should observation status utilization be measured and what benchmarks should be used to monitor results? If we see an increase in observation utilization is it a desired outcome or should we be concerned? The typical practice of measuring billed units may not be the best approach since observation stays are billed using different units depending on how payer hospital contracts are designed so some observation services may be billed in hourly increments in some settings, in 24 hour increments in others and as a generic per observation episode in others. Our recommendation is to use “observation episodes” as the unit for measuring utilization. An observation episode is measured using the same logic as an inpatient stay, each midnight occurring during an observation stay counts as 1 observation unit with a minimum value of 1 for observation stay not spanning midnight. As examples, an observation stay starting at 4 AM and ending at 8 PM would be 1 observation unit, a stay starting at 4 AM and ending at 1 AM would also be 1 observation unit and finally a stay starting at 4 AM and ending at 1 AM after two midnight would be 2 observation units. This allows us to directly compare observation unit utilization with inpatient hospital utilization and one goal for directing patients towards observation is to reduce inpatient utilization.
Now for the question of how do we determine if our rate for observation utilization is positive or a problem we need to address. Since observation is a substitute for short stay hospital admissions we need to look both at observation utilization and short stay hospital utilization. We recommend combining observation episode with 1 day LOS hospital admissions to produce a combined utilization rate and then comparing this to either a historical target or a benchmark from a source such as Milliman. An example taken from a Milliman analytic tool (MedInsight Guideline Analytics) is shown below.
Using this example the target utilization is the “combined Obs + 1 Day LOS” or 27.7/1000. Our actual is 28.3 or a bit higher than we would like. In addition, our 1 Day LOS utilization is higher than the benchmark while our Obs utilization is less meaning we should have opportunity to shift more 1 day admits cases to Obs without causes excess utilization.
Avoidable emergency room (ER) visits are a critical component to cost effective population management. Frequent and potentially avoidable use of hospital ERs is extremely costly, especially when care may be able to be provided in a less expensive setting.
In a patient centered medical home (PCMH) reporting project performed by Milliman, ER utilization was one of several utilization targets. Specific to ER utilization, several measures were identified to allow for a broad understanding of the characteristics of those visiting the ER. These measures compared the pre PCMH enrollment period as well as the post PCMH enrollment period. Also included in the analysis were member details such as age, diagnosis, and dates of visits. Milliman performed drill-down analysis to identify opportunities for cost reduction, such as those members who visit the ER frequently (e.g. 3 or more visits within 4 months). The analysis also provided insight into access and care management issues using the day of the week the ER visits were occurring.
While evaluating the characteristics of the ER population, integrating it with potentially avoidable ED visits can be accomplished using different methodologies. In this project, the New York University (NYU) algorithm of potentially avoidable ER visits was used.
From the claims data, this algorithm uses the diagnosis of patients of those who visited the emergency department and categorizes them into defined categories. The primary categories that address the potentially avoidable ED visits are:
- Primary Care Treatable
All ED visits for primary diagnosis of injury, mental health problems, alcohol, or substance abuse are also classified into separate categories but will not be addressed within this blog post. Please see the links below for additional detail.
Another option for identifying avoidable ER visits is to evaluate the diagnosis codes against those diagnoses identified as Avoidable Visit diagnosis codes by the MediCal Managed Care Division. Please see the link below for more detail.
- New York University Roger F. Wagner School of Public Service
NYU ED Algorithm Articles, NYU ED Algorithm
Emergency Department Use: The New York Story
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