Factors associated with poor self-management documented in home health care narrative notes for patients with heart failure

BACKGROUND

Patients with heart failure (HF) who actively engage in their own self-management have better outcomes. Extracting data through natural language processing (NLP) holds great promise for identifying patients with or at risk of poor self-management.

OBJECTIVE

To identify home health care (HHC) patients with HF who have poor self-management using NLP of narrative notes, and to examine patient factors associated with poor self-management.

METHODS

An NLP algorithm was applied to extract poor self-management documentation using 353,718 HHC narrative notes of 9,710 patients with HF. Sociodemographic and structured clinical data were incorporated into multivariate logistic regression models to identify factors associated with poor self-management.

RESULTS

There were 758 (7.8%) patients in this sample identified as having notes with language describing poor HF self-management. Younger age (OR 0.982, 95% CI 0.976-0.987, p < .001), longer length of stay in HHC (OR 1.036, 95% CI 1.029- 1.043, p < .001), diagnosis of diabetes (OR 1.47, 95% CI 1.3-1.67, p < .001) and depression (OR 1.36, 95% CI 1.09-1.68, p < .01), impaired decision-making (OR 1.64, 95% CI 1.37-1.95, p < .001), smoking (OR 1.7, 95% CI 1.4-2.04, p < .001), and shortness of breath with exertion (OR 1.25, 95% CI 1.1-1.42, p < .01) were associated with poor self-management.

CONCLUSIONS

Patients with HF who have poor self-management can be identified from the narrative notes in HHC using novel NLP methods. Meaningful information about the self-management of patients with HF can support HHC clinicians in developing individualized care plans to improve self-management and clinical outcomes.

Detecting Language Associated With Home Healthcare Patient's Risk for Hospitalization and Emergency Department Visit

BACKGROUND

About one in five patients receiving home healthcare (HHC) services are hospitalized or visit an emergency department (ED) during a home care episode. Early identification of at-risk patients can prevent these negative outcomes. However, risk indicators, including language in clinical notes that indicate a concern about a patient, are often hidden in narrative documentation throughout their HHC episode.

OBJECTIVE

The aim of the study was to develop an automated natural language processing (NLP) algorithm to identify concerning language indicative of HHC patients' risk of hospitalizations or ED visits.

METHODS

This study used the Omaha System-a standardized nursing terminology that describes problems/signs/symptoms that can occur in the community setting. First, five HHC experts iteratively reviewed the Omaha System and identified concerning concepts indicative of HHC patients' risk of hospitalizations or ED visits. Next, we developed and tested an NLP algorithm to identify these concerning concepts in HHC clinical notes automatically. The resulting NLP algorithm was applied on a large subset of narrative notes (2.3 million notes) documented for 66,317 unique patients ( n = 87,966 HHC episodes) admitted to one large HHC agency in the Northeast United States between 2015 and 2017.

RESULTS

A total of 160 Omaha System signs/symptoms were identified as concerning concepts for hospitalizations or ED visits in HHC. These signs/symptoms belong to 31 of the 42 available Omaha System problems. Overall, the NLP algorithm showed good performance in identifying concerning concepts in clinical notes. More than 18% of clinical notes were detected as having at least one concerning concept, and more than 90% of HHC episodes included at least one Omaha System problem. The most frequently documented concerning concepts were pain, followed by issues related to neuromusculoskeletal function, circulation, mental health, and communicable/infectious conditions.

CONCLUSION

Our findings suggest that concerning problems or symptoms that could increase the risk of hospitalization or ED visit were frequently documented in narrative clinical notes. NLP can automatically extract information from narrative clinical notes to improve our understanding of care needs in HHC. Next steps are to evaluate which concerning concepts identified in clinical notes predict hospitalization or ED visit.

Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians

OBJECTIVE

To identify the risk factors home healthcare (HHC) clinicians associate with patient deterioration and understand how clinicians respond to and document these risk factors.

METHODS

We interviewed multidisciplinary HHC clinicians from January to March of 2021. Risk factors were mapped to standardized terminologies (eg, Omaha System). We used directed content analysis to identify risk factors for deterioration. We used inductive thematic analysis to understand HHC clinicians' response to risk factors and documentation of risk factors.

RESULTS

Fifteen HHC clinicians identified a total of 79 risk factors that were mapped to standardized terminologies. HHC clinicians most frequently responded to risk factors by communicating with the prescribing provider (86.7% of clinicians) or following up with patients and caregivers (86.7%). HHC clinicians stated that a majority of risk factors can be found in clinical notes (ie, care coordination (53.3%) or visit (46.7%)).

DISCUSSION

Clinicians acknowledged that social factors play a role in deterioration risk; but these factors are infrequently studied in HHC. While a majority of risk factors were represented in the Omaha System, additional terminologies are needed to comprehensively capture risk. Since most risk factors are documented in clinical notes, methods such as natural language processing are needed to extract them.

CONCLUSION

This study engaged clinicians to understand risk for deterioration during HHC. The results of our study support the development of an early warning system by providing a comprehensive list of risk factors grounded in clinician expertize and mapped to standardized terminologies.

Home Healthcare Clinicians' Perspectives on Electronic Health Records: A Qualitative Study

Clinicians' perspectives on the electronic health records (EHR) in home healthcare (HHC) are understudied. To explore this topic, qualitative interviews were conducted with 15 HHC clinicians in the Northeastern USA. Thematic analysis was conducted to identify key themes emerging from the interviews. While some EHR benefits were recognized, overall satisfaction with the EHR was low. The results suggest EHR limitations are tied to poor usability, restrictions, and redundancy in documentation leading to increased documentation workload. Clinicians have recommendations to mitigate these limitations via additional EHR functions and better patient risk detection. Future stakeholders should consider the results of this study when developing and updating the EHR in HHC.

Identifying Heart Failure Symptoms and Poor Self-Management in Home Healthcare: A Natural Language Processing Study

The goal of this natural language processing (NLP) study was to identify patients in home healthcare with heart failure symptoms and poor self-management (SM). The preliminary lists of symptoms and poor SM status were identified, NLP algorithms were used to refine the lists, and NLP performance was evaluated using 2.3 million home healthcare clinical notes. The overall precision to identify patients with heart failure symptoms and poor SM status was 0.86. The feasibility of methods was demonstrated to identify patients with heart failure symptoms and poor SM documented in home healthcare notes. This study facilitates utilizing key symptom information and patients' SM status from unstructured data in electronic health records. The results of this study can be applied to better individualize symptom management to support heart failure patients' quality-of-life.

Predictive Risk Models for Wound Infection-Related Hospitalization or ED Visits in Home Health Care Using Machine-Learning Algorithms

OBJECTIVE

Wound infection is prevalent in home healthcare (HHC) and often leads to hospitalizations. However, none of the previous studies of wounds in HHC have used data from clinical notes. Therefore, the authors created a more accurate description of a patient's condition by extracting risk factors from clinical notes to build predictive models to identify a patient's risk of wound infection in HHC.

METHODS

The structured data (eg, standardized assessments) and unstructured information (eg, narrative-free text charting) were retrospectively reviewed for HHC patients with wounds who were served by a large HHC agency in 2014. Wound infection risk factors were identified through bivariate analysis and stepwise variable selection. Risk predictive performance of three machine learning models (logistic regression, random forest, and artificial neural network) was compared.

RESULTS

A total of 754 of 54,316 patients (1.39%) had a hospitalization or ED visit related to wound infection. In the bivariate logistic regression, language describing wound type in the patient's clinical notes was strongly associated with risk (odds ratio, 9.94; P < .05). The areas under the curve were 0.82 in logistic regression, 0.75 in random forest, and 0.78 in artificial neural network. Risk prediction performance of the models improved (by up to 13.2%) after adding risk factors extracted from clinical notes.

CONCLUSIONS

Logistic regression showed the best risk prediction performance in prediction of wound infection-related hospitalization or ED visits in HHC. The use of data extracted from clinical notes can improve the performance of risk prediction models.

Infection prevention and control practices in the home environment: Examining enablers and barriers to adherence among home health care nurses

BACKGROUND

Infection Prevention and Control (IPC) practices have been established in home health care. Adherence to IPC practices has been suboptimal with limited available evidence. The study aim was to examine the impact of individual, home environment, and organizational factors on IPC practices using human factors model.

METHODS

Three hundred and fifty-three nurses were surveyed across two large home care agencies to examine the relationship between IPC adherence and individual, home environment, and organizational factors.

RESULTS

Nurses reported multiple barriers to IPC practices in patients' homes (mean = 4.34, standard deviation = 2.53). Frequent barriers included clutter (reported by 74.5% of nurses) and a dirty environment (70.3%). Nurses also reported limited availability of some IPC supplies (mean = 7.76, standard deviation = 2.44), including personal protective equipment. Home environment factors were significant barriers, and availability of IPC supplies were significant enablers of IPC adherence. Agency-provided training and decision-making resources were not significant factors for IPC adherence in the presence of home environment barriers and IPC supplies.

CONCLUSIONS

This study findings suggest that IPC adherence strategies point to addressing barriers in the home environment and increasing availability of IPC supplies. The relationship between the patient's home environment, organizational factors, and IPC practices among home health care nurses warrants further study.

Serotypes of Pseudomonas aeruginosa from human and animal sources

Forty human isolates and twenty-eight animal isolates of Pseudomonas aeruginosa from Prince Edward Island area (Atlantic Canada) were serotyped using a kit consisting of 4 polyvalent O-group antisera and 17 monovalent O-type (serovar) antisera, in accordance with the International Antigenic Typing Scheme. The results showed a difference between humans/dogs (50%/48% group I) and pigs (71% group IV). Whereas O-serovar 9 and 1 appeared to be most involved in human and canine infections, O-serovar 3 was the predominant type in porcine infections.