Ambulatory visit groups: an outpatient classification system

In-Hospital Cardiac Arrest in United States Emergency Departments, 2010–2018

Background

Little is known about the in-hospital cardiac arrest (IHCA) in the US emergency department (ED). This study aimed to describe the incidence and mortality of ED-based IHCA visits and to investigate the factors associated with higher incidence and poor outcomes of IHCA.

Materials and Methods

Data were obtained from the National Hospital Ambulatory Medical Care Survey (NHAMCS) between 2010 and 2018. Adult ED visits with IHCA were identified using the cardiopulmonary resuscitation code, excluding those with out-of-hospital cardiac arrest. We used descriptive statistics and multivariable logistic regression accounting for NHAMCS’s complex survey design. The primary outcome measures were ED-based IHCA incidence rates and ED-based IHCA mortality.

Results

Over the 9-year study period, there were approximately 1,114,000 ED visits with IHCA. The proportion of IHCA visits in the entire ED population (incidence rate, 1.2 per 1,000 ED visits) appeared stable. The mean age of patients who visited the ED with IHCA was 60 years, and 65% were men. Older age, male, arrival by ambulance, and being uninsured independently predicted a higher incidence of ED-based IHCA. Approximately 51% of IHCA died in the ED, and the trend remained stable. Arrival by ambulance, nighttime, or weekend arrival, and being in the non-Northeast were independently associated with a higher mortality rate after IHCA.

Conclusion

The high burden of ED visits with IHCA persisted through 2010–2018. Additionally, ED-based IHCA survival to hospital admission remained poor. Some patients were disproportionately affected, and certain contextual factors were associated with a poorer outcome.

A mathematical theory for identifying and measuring severity of episodes of care

OBJECTIVES

We propose and test a method for constructing episodes of care from data within administrative databases and electronic health records.

SUBJECTS

We created a measure for severity of episodes of illness for 565 randomly chosen developmentally delayed children who were enrolled in the Medicaid program.

DESIGN

Regression analysis was conducted to test the percentage of variance explained by our proposed mathematical model in cost of care.

DATA COLLECTION

Data included both hospitalizations and clinic visits obtained from Medicaid programs from one southeastern state.

METHODS

For each patient, the likelihood that two diagnoses are part of the same episode is proportional to the similarity of the two diagnoses and to the short time interval between them. When this likelihood exceeds a preset cutoff, then the two diagnoses are part of the same episode. The cutoff is estimated by selecting number of days before two very similar diagnoses are considered to be part of separate episodes. The similarity between two diagnoses is assumed to be proportional to co-occurrence of the two diagnoses within a fixed period (usually 30 days). The severity of an episode was calculated using a Muliplicative Multiattribute Utility model, where severity of each diagnosis is aggregated to estimate the overall severity of the episode. Severity of each diagnosis was assumed to be proportional to average cost of a diagnosis-if patients do not die before care is delivered. The article includes an algorithm that can classify a patient's diagnosis into episodes of care and measure severity of the episodes from date of diagnoses, code for the diagnoses, and charges for the visit. To facilitate integration with existing database, the article includes a Standard Query Language computer program. To evaluate the method of constructing episodes of care, we regressed cost of care on the patient's number of episodes of care within the year, average severity of the episodes within the year, and the interaction between number and average severity of the episodes.

RESULTS

The number of episodes (alpha = .001), the average severity of the episodes (alpha = .001), and the product of the two (alpha = .001) had statistically significant relationships to the average cost of the case. The 3 variables together explained 53% of variation in yearly cost of care.

CONCLUSIONS

These data suggest that our proposed mathematical approach is reasonable and produces severity scores that are predictive of objective criteria such as cost of care.

Medical care classification systems in the ambulatory care environment: an evaluative framework

An essential need of a Quality Assurance Program (QAP) in an ambulatory care setting is accurate and reliable information characterizing the encounter between patient and provider. This information includes identification of the patient's reason for visit, the provider's diagnostic impressions, and procedures performed. Such data can be used to maintain an informational index on the nature of patient care. Based upon this index, a wide variety of special studies in the assessment and assurance of the quality of care can be conducted. A primary consideration in the development of such an index is the choice of a Medical Care Classification System (MCCS) to be used to code encounter-related data. The objective of this paper is to provide a methodology and evaluative framework in which available Medical Care Classification Systems can be assessed as to which one best meets the needs of the quality assurance programs within ambulatory care settings.