John Welton

Is there a business case for magnet hospitals? Estimates of the cost and revenue implications of becoming a magnet.

Background:Although Magnet hospitals (MHs) are known for their better nursing care environments, little is known about whether MHs achieve this at a higher (lower) cost of health care or whether a superior nursing environment yields higher net patient revenue versus non-MHs over an extended period of time. Objective:To examine how achieving Magnet status is related to subsequent inpatient costs and revenues controlling for other hospital characteristics. Data and Methods:Data from the American Hospital Association Annual Survey, Hospital Cost Reporting Information System reports collected by Centers for Medicare & Medicaid Services, and Magnet status of hospitals from American Nurses Credentialing Center from 1998 to 2006 were combined and used for the analysis. Descriptive statistics, propensity score matching, fixed-effect, and instrumental variable methods were used to analyze the data. Results:Regression analyses revealed that MH status is positively and significantly associated with both inpatient costs and net inpatient revenues for both urban hospitals and all hospitals. MH status was associated with an increase of 2.46% in the inpatient costs and 3.89% in net inpatient revenue for all hospitals, and 2.1% and 3.2% for urban hospitals. Conclusions:Although it is costly for hospitals to attain Magnet status, the cost of becoming a MH may be offset by higher net inpatient income. On average, MHs receive an adjusted net increase in inpatient income of

Diagnostic Imaging Services in Magnet and Non-Magnet Hospitals: Trends in Utilization and Costs

104.22–

Prevalence of nursing diagnoses as a measure of nursing complexity in a hospital setting

127.05 per discharge after becoming a Magnet which translates to an additional

Using a Data Quality Framework to Clean Data Extracted from the Electronic Health Record: A Case Study.

1,229,770–

Nurse staffing and inpatient mortality: is the question outcomes or nursing value?

1,263,926 in income per year.

A call to action: Engage in big data science

PURPOSEnThe purpose of this study was to better understand trends in utilization and costs of diagnostic imaging services at Magnet hospitals (MHs) and non-Magnet hospitals (NMHs).nnnMETHODSnA data set was created by merging hospital-level data from the American Hospital Associations annual survey and Medicare cost reports, individual-level inpatient data from the Healthcare Cost and Utilization Project, and Magnet recognition status data from the American Nurses Credentialing Center. A descriptive analysis was conducted to evaluate the trends in utilization and costs of CT, MRI, and ultrasound procedures among MHs and NMHs in urban locations between 2000 and 2006 from the following ten states: Arizona, California, Colorado, Florida, Iowa, Maryland, North Carolina, New Jersey, New York, and Washington.nnnRESULTSnWhen matched by bed size, severity of illness (case mix index), and clinical technological sophistication (Saidin index) quantiles, MHs in higher quantiles indicated higher rates of utilization of imaging services for MRI, CT, and ultrasound in comparison with NMHs in the same quantiles. However, average costs of MRI, CT, and ultrasounds were lower at MHs in comparison with NMHs in the same quantiles.nnnCONCLUSIONSnOverall, MHs that are larger in size (number of beds), serve more severely ill patients (case mix index), and are more technologically sophisticated (Saidin index) show higher utilization of diagnostic imaging services, although costs per procedure at MHs are lower in comparison with similar NMHs, indicating possible cost efficiency at MHs. Further research is necessary to understand the relationship between the utilization of diagnostic imaging services among MHs and its impact on patient outcomes.

Measuring Nursing Care Value.

AIMSnTo describe the prevalence of nursing diagnoses on admission among inpatient units and medical diagnoses and to analyse the relationship of nursing diagnoses to patient characteristics and hospital outcomes.nnnBACKGROUNDnNursing diagnoses classify patients according to nursing dependency and can be a measure of nursing complexity. Knowledge regarding the prevalence of nursing diagnoses on admission and their relationship with hospital outcomes is lacking.nnnDESIGNnProspective observational study.nnnMETHODSnData were collected for 6xa0months in 2014 in four inpatient units of an Italian hospital using a nursing information system and the hospital discharge register. Nursing diagnoses with prevalence higher or equal to 20% were considered as high frequency. Nursing diagnoses with statistically significant relationships with either higher mortality or length of stay were considered as high risk. The high-frequency/high-risk category of nursing diagnoses was identified.nnnRESULTSnThe sample included 2283 patients. A mean of 4·5 nursing diagnoses per patient was identified; this number showed a statistically significant difference among inpatient units and medical diagnoses. Six nursing diagnoses were classified as high frequency/high risk. Nursing diagnoses were not correlated with patient gender and age. A statistically significant perfect linear association (Spearmans correlation coefficient) was observed between the number of nursing diagnoses and both the length of stay and the mortality rate.nnnCONCLUSIONnNursing complexity, as described by nursing diagnoses, was shown to be associated with length of stay and mortality. These results should be confirmed after considering other variables through multivariate analyses. The concept of high-frequency/high-risk nursing diagnoses should be expanded in further studies.

Risk-Adjusted Staffing to Improve Patient Value.

Objectives: We examine the following: (1) the appropriateness of using a data quality (DQ) framework developed for relational databases as a data-cleaning tool for a data set extracted from two EPIC databases, and (2) the differences in statistical parameter estimates on a data set cleaned with the DQ framework and data set not cleaned with the DQ framework. Background: The use of data contained within electronic health records (EHRs) has the potential to open doors for a new wave of innovative research. Without adequate preparation of such large data sets for analysis, the results might be erroneous, which might affect clinical decision-making or the results of Comparative Effectives Research studies. Methods: Two emergency department (ED) data sets extracted from EPIC databases (adult ED and children ED) were used as examples for examining the five concepts of DQ based on a DQ assessment framework designed for EHR databases. The first data set contained 70,061 visits; and the second data set contained 2,815,550 visits. SPSS Syntax examples as well as step-by-step instructions of how to apply the five key DQ concepts these EHR database extracts are provided. Conclusions: SPSS Syntax to address each of the DQ concepts proposed by Kahn et al. (2012)1 was developed. The data set cleaned using Kahn’s framework yielded more accurate results than the data set cleaned without this framework. Future plans involve creating functions in R language for cleaning data extracted from the EHR as well as an R package that combines DQ checks with missing data analysis functions.

Nurse staffing and patient outcomes: Are we asking the right research question?

In this issue of Medical Care, Dr Linda Aiken and colleagues from the University of Pennsylvania publish their results from a multistate study and report hospitals with better patient-to-nurse ratios and higher percent of bachelor degree (BSN) prepared nurses, conditional on a good work environment,