No abstract

BACKGROUND: Poor communication of tests whose results are pending at hospital discharge can lead to medical errors.OBJECTIVE: To determine the adequacy with which hospital discharge summaries document tests with pending results and the appropriate follow-up providers. DESIGN:Retrospective study of a randomly selected sample PATIENTS: Six hundred ninety-six patients discharged from two large academic medical centers, who had test results identified as pending at discharge through queries of electronic medical records. INTERVENTION AND MEASUREMENTS:Each patient's discharge summary was reviewed to identify whether information about pending tests and follow-up providers was mentioned. Factors associated with documentation were explored using clustered multivariable regression models.MAIN RESULTS: Discharge summaries were available for 99.2% of 668 patients whose data were analyzed. These summaries mentioned only 16% of tests with pending results (482 of 2,927). Even though all study patients had tests with pending results, only 25% of discharge summaries mentioned any pending tests, with 13% documenting all pending tests. The documentation rate for pending tests was not associated with level of experience of the provider preparing the summary, patient's age or race, length of hospitalization, or duration it took for results to return. Follow-up providers' information was documented in 67% of summaries.CONCLUSION: Discharge summaries are grossly inadequate at documenting both tests with pending results and the appropriate follow-up providers.KEY WORDS: tests with pending results; continuity of care; patient safety; discharge summary; medical errors.

In Electronic Health Records (EHRs), much of valuable information regarding patients’ conditions is embedded in free text format. Natural language processing (NLP) techniques have been developed to extract clinical information from free text. One challenge faced in clinical NLP is that the meaning of clinical entities is heavily affected by modifiers such as negation. A negation detection algorithm, NegEx, applies a simplistic approach that has been shown to be powerful in clinical NLP. However, due to the failure to consider the contextual relationship between words within a sentence, NegEx fails to correctly capture the negation status of concepts in complex sentences. Incorrect negation assignment could cause inaccurate diagnosis of patients’ condition or contaminated study cohorts. We developed a negation algorithm called DEEPEN to decrease NegEx’s false positives by taking into account the dependency relationship between negation words and concepts within a sentence using Stanford dependency parser. The system was developed and tested using EHR data from Indiana University (IU) and it was further evaluated on Mayo Clinic dataset to assess its generalizability. The evaluation results demonstrate DEEPEN, which incorporates dependency parsing into NegEx, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs.

DKA episodes represented more than $1 of every $4 spent on direct medical care for adult patients with type I diabetes and $1 of every $2 in those patients experiencing multiple episodes. Interventions that are capable of even a modest reduction in the number of DKA episodes could produce substantial cost savings in a health care system and could be particularly cost-effective in adult patients with recurrent DKA.

A retrospective cohort study was conducted including 3,688 patients age 60 years or older without dementia enrolled in a depression screening study in primary care clinics. Information on antidepressant use and incident dementia during follow-up was retrieved from electronic medical records. Cox’s proportional hazard models were used to compare the risk for incident dementia among five participant groups: SSRI only, non-SSRI only (Non-SSRI), mixed group of SSRI and non-SSRI, not on antidepressants but depressed, and not on antidepressants and not depressed. SSRI and Non-SSRI users had significantly higher dementia risk than the non-depressed non-users (HR=1.83, p=0.0025 for SSRI users and HR=1.50, p=0.004 for non-SSRI users). In addition, SSRIs users had significantly higher dementia risk than non-users with severe depression (HR=2.26, p=0.0005). Future research is needed to confirm our results in other populations and to explore potential mechanism underlying the observed association.

Patients with symptoms of depression accrue greater average diagnostic test charges. However, these data suggest that such patients also have a greater burden of comorbid nonpsychiatric illness. Efforts to improve outcome and decrease cost for patients who have late-life depression must target interventions to improve the care of psychiatric and medical illness concurrently.

Visit-to-visit blood pressure variability has received considerable attention recently. The objective of our study is to define a variability measure that is independent of change over time and determine the association between longitudinal summary measures of blood pressure measurements and mortality risk. Data for the study came from a prospective cohort of 2,906 adults, age 60 or older, in an urban primary care system with up to fifteen years follow-up. Dates of death for deceased participants were retrieved from the National Death Index. Systolic and diastolic blood pressure measurements from outpatient clinic visits were extracted from the Regenstrief Medical Record System. For each patient, the intercept, regression slope, and root mean square error for visit-to-visit variability were derived using linear regression models and used as independent variables in Cox's proportional hazards models for both all-cause mortality and mortality due to coronary heart disease or stroke. Rate of change was associated with mortality risk in a U-shaped relationship and that participants with little or no change in blood pressure had the lowest mortality risk. Blood pressure variability was not an independent predictor of mortality risk. By separating change over time from visit-to-visit variability in studies with relatively long follow-up, we demonstrated in this elderly primary care patient population that blood pressure changes over time, not variability, were associated with greater mortality risk. Future research is needed to confirm our findings in other populations.

As of 1995 an overwhelming majority of schizophrenic patients in this indigent, inner-city population initiated therapy with a typical antipsychotic. Patients frequently switched antipsychotics and discontinued their therapy during the 1 year study period. Reasons for switching or discontinuing may include the following: ineffective therapy; patient intolerance; change in symptoms; and improved assessment and understanding of the diagnosis or physician preference.