Availability and Quality of Information Used by Nurses While Admitting Patients to a Rural Home Health Care Agency

Medication errors' causes analysis in home care setting: A systematic review

Many studies analyze the medication errors in the hospital setting, but the literature involving the home care setting seems scarce. The aim of this study is to identify the main risk factors that affect the genesis of medication errors and the possible solutions to reduce the phenomenon in the home care setting. This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The critical analysis of the literature shows that medication errors in home care occur mainly during transitional care. The main risk factors related to transitional care are poor interprofessional communication, lack of a standardized process for medication reconciliation, the widespread use of computerized tools, and the inadequate integration of the pharmacist into the care team. The strategies to reduce the risk of errors from therapy at home are the implementation of the pharmacist in the health team to ensure accurate medication reconciliation and the use of computerized tools to improve communication between professionals and to reduce the dispersion of information.

Free-Text Documentation of Dementia Symptoms in Home Healthcare: A Natural Language Processing Study

Background

Little is known about symptom documentation related to Alzheimer's disease and related dementias (ADRD) by home healthcare (HHC) clinicians.

Objective

This study: (1) developed a natural language processing (NLP) algorithm that identifies common neuropsychiatric symptoms of ADRD in HHC free-text clinical notes; (2) described symptom clusters and hospitalization or emergency department (ED) visit rates for patients with and without these symptoms.

Method

We examined a corpus of -2.6 million free-text notes for 112,237 HHC episodes among 89,459 patients admitted to a non-profit HHC agency for post-acute care with any diagnosis. We used NLP software (NimbleMiner) to construct indicators of six neuropsychiatric symptoms. Structured HHC assessment data were used to identify known ADRD diagnoses and construct measures of hospitalization/ED use during HHC.

Results

Neuropsychiatric symptoms were documented for 40% of episodes. Common clusters included impaired memory, anxiety and/or depressed mood. One in three episodes without an ADRD diagnosis had documented symptoms. Hospitalization/ED rates increased with one or more symptoms present.

Conclusion

HHC providers should examine episodes with neuropsychiatric symptoms but no ADRD diagnoses to determine whether ADRD diagnosis was missed or to recommend ADRD evaluation. NLP-generated symptom indicators can help to identify high-risk patients for targeted interventions.