Gregg Husk

Differences in diagnostic evaluation and clinical outcomes in the care of patients with chest pain based on admitting service: the benefits of a dedicated chest pain unit.

BackgroundChest pain is one of the most common complaints of patients presenting at emergency departments. However, the most appropriate diagnostic evaluation for patients with chest pain but without acute coronary syndrome remains controversial, and differs greatly among institutions and physicians. At our institution, patients with chest pain can be admitted to an internist-run hospitalist service, a private attending service, or a cardiologist-run Chest Pain Unit. The goal of the present study was to compare the management and outcomes of patients admitted with chest pain based on admitting service.MethodsThe charts of 750 patients (250 consecutive patients per service) with a discharge diagnosis of chest pain were studied retrospectively.ResultsPatients admitted to the Chest Pain Unit were younger and had a lower prevalence of known coronary artery disease, hypertension, or diabetes, but a similar prevalence of other risk factors compared with the other groups. Sixty percent of the patients in the Chest Pain Unit underwent stress myocardial perfusion imaging as their primary diagnostic modality (vs 22% and 12% of patients in the hospitalist and private services, respectively; P<.001). In contrast, 35% of the patients admitted to the hospitalist service underwent rest echocardiography (vs 8% and 17% of patients in the Chest Pain Unit and private services, respectively; P<.001). Finally, 47% of the patients in the private service underwent coronary angiography as their primary diagnostic modality (vs 6% and 10% of patients in the Chest Pain Unit and hospitalist services, respectively; P<.001). The length of stay was shortest for patients in the Chest Pain Unit (1.4±1.2 days vs 3.9±3.4 days and 3.5±3.6 days in the hospitalist and private services, respectively; P<.001), even when corrected for patient age and number of risk factors. Readmission within 6 months was lowest for patients in the Chest Pain Unit (4.4% vs 17.6% and 15.2% in the hospitalist and private services, respectively; P<.001).ConclusionsThe results of this study demonstrate that a highly protocolized chest pain unit, using myocardial perfusion imaging as primary diagnostic modality, results in a decreased length of stay and readmission rate.

Fluoroquinolone- and ceftriaxone-based therapy of community-acquired pneumonia in hospitalized patients: The risk of subsequent isolation of multidrug-resistant organisms

A retrospective cohort study was performed on 175 adult patients treated for community-acquired pneumonia with moxifloxacin or ceftriaxone/azithromycin in a nonintensive care unit. Both cohorts were very similar with regard to a wide range of characteristics including age, severity of disease, comorbidities, length of stay, and mortality. Multidrug-resistant organisms were subsequently isolated from 6 (15%) moxifloxacin-treated patients and 5 (4%) ceftriaxone/azithromycin-treated patients within 90 days after beginning of therapy (P = .026 on logistic regression analysis).

Hospital Closure and Insights into Patient Dispersion: The Closure of Saint Vincent’s Catholic Medical Center in New York City

BACKGROUND Hospital closures are becoming increasingly common in the United States. Patients who received care at the closing hospitals must travel to different, often farther hospitals for care, and nearby remaining hospitals may have difficulty coping with a sudden influx of patients. OBJECTIVES Our objectives are to analyze the dispersion patterns of patients from a closing hospital and to correlate that with distance from the closing hospital for three specific visit types: emergency, inpatient, and ambulatory. METHODS In this study, we used data from a health information exchange to track patients from Saint Vincents Medical Center, a hospital in New York City that closed in 2010, to determine where they received emergency, inpatient, and ambulatory care following the closure. RESULTS We found that patients went to the next nearest hospital for their emergency and inpatient care, but ambulatory encounters did not correlate with distance. DISCUSSION It is likely that patients followed their ambulatory providers as they transitioned to another hospital system. Additional work should be done to determine predictors of impact on nearby hospitals when another hospital in the community closes in order to better prepare for patient dispersion.

Measuring the Degree of Unmatched Patient Records in a Health Information Exchange Using Exact Matching

BACKGROUND Health information exchange (HIE) facilitates the exchange of patient information across different healthcare organizations. To match patient records across sites, HIEs usually rely on a master patient index (MPI), a database responsible for determining which medical records at different healthcare facilities belong to the same patient. A single patients records may be improperly split across multiple profiles in the MPI. OBJECTIVES We investigated the how often two individuals shared the same first name, last name, and date of birth in the Social Security Death Master File (SSDMF), a US government database containing over 85 million individuals, to determine the feasibility of using exact matching as a split record detection tool. We demonstrated how a method based on exact record matching could be used to partially measure the degree of probable split patient records in the MPI of an HIE. METHODS We calculated the percentage of individuals who were uniquely identified in the SSDMF using first name, last name, and date of birth. We defined a measure consisting of the average number of unique identifiers associated with a given first name, last name, and date of birth. We calculated a reference value for this measure on a subsample of SSDMF data. We compared this measure value to data from a functioning HIE. RESULTS We found that it was unlikely for two individuals to share the same first name, last name, and date of birth in a large US database including over 85 million individuals. 98.81% of individuals were uniquely identified in this dataset using only these three items. We compared the value of our measure on a subsample of Social Security data (1.00089) to that of HIE data (1.1238) and found a significant difference (t-test p-value < 0.001). CONCLUSIONS This method may assist HIEs in detecting split patient records.