A Predictive Risk Model for Infection-Related Hospitalization Among Home Healthcare Patients

Enhancing readmission prediction models by integrating insights from home healthcare notes: Retrospective cohort study

BACKGROUND

Hospital readmission is an important indicator of inpatient care quality and a significant driver of increasing medical costs. Therefore, it is important to explore the effects of postdischarge information, particularly from home healthcare notes, on enhancing readmission prediction models. Despite the use of Natural Language Processing (NLP) and machine learning in prediction model development, current studies often overlook insights from home healthcare notes.

OBJECTIVE

This study aimed to develop prediction models for 30-day readmissions using home healthcare notes and structured data. In addition, it explored the development of 14- and 180-day prediction models using variables in the 30-day model.

DESIGN

A retrospective observational cohort study.

SETTING(S)

This study was conducted at Ajou University School of Medicine in South Korea.

PARTICIPANTS

Data from electronic health records, encompassing demographic characteristics of 1819 participants, along with information on conditions, drug, and home healthcare, were utilized.

METHODS

Two distinct models were developed for each prediction window (30-, 14-, 180-day): the traditional model, which utilized structured variables alone, and the common data model (CDM)-NLP model, which incorporated structured and topic variables extracted from home healthcare notes. BERTopic facilitated topic generation and risk probability, representing the likelihood of documents being assigned to specific topics. Feature selection involved experimenting with various algorithms. The best-performing algorithm, determined using the area under the receiver operating characteristic curve (AUROC), was used for model development. Model performance was assessed using various learning metrics including AUROC.

RESULTS

Among 1819 patients, 251 (13.80 %) experienced 30-day readmission. The least absolute shrinkage and selection operator was used for feature extraction and model development. The 15 structured features were used in the traditional model. Moreover, five additional topic variables from the home healthcare notes were applied in the CDM-NLP model. The AUROC of the traditional model was 0.739 (95 % CI: 0.672-0.807). The AUROC of the CDM-NLP model was high at 0.824 (95 % CI: 0.768-0.880), which indicated an outstanding performance. The topics in the CDM-NLP model included emotional distress, daily living functions, nutrition, postoperative status, and cardiorespiratory issues. In extended prediction model development for 14- and 180-day readmissions, the CDM-NLP consistently outperformed the traditional model.

CONCLUSIONS

This study developed effective prediction models using both structured and unstructured data, thereby emphasizing the significance of postdischarge information from home healthcare notes in readmission prediction.

Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient-nurse verbal communications in home healthcare settings?

BACKGROUND

Identifying health problems in audio-recorded patient-nurse communication is important to improve outcomes in home healthcare patients who have complex conditions with increased risks of hospital utilization. Training machine learning classifiers for identifying problems requires resource-intensive human annotation.

OBJECTIVE

To generate synthetic patient-nurse communication and to automatically annotate for common health problems encountered in home healthcare settings using GPT-4. We also examined whether augmenting real-world patient-nurse communication with synthetic data can improve the performance of machine learning to identify health problems.

DESIGN

Secondary data analysis of patient-nurse verbal communication data in home healthcare settings.

METHODS

The data were collected from one of the largest home healthcare organizations in the United States. We used 23 audio recordings of patient-nurse communications from 15 patients. The audio recordings were transcribed verbatim and manually annotated for health problems (e.g., circulation, skin, pain) indicated in the Omaha System Classification scheme. Synthetic data of patient-nurse communication were generated using the in-context learning prompting method, enhanced by chain-of-thought prompting to improve the automatic annotation performance. Machine learning classifiers were applied to three training datasets: real-world communication, synthetic communication, and real-world communication augmented by synthetic communication.

RESULTS

Average F1 scores improved from 0.62 to 0.63 after training data were augmented with synthetic communication. The largest increase was observed using the XGBoost classifier where F1 scores improved from 0.61 to 0.64 (about 5% improvement). When trained solely on either real-world communication or synthetic communication, the classifiers showed comparable F1 scores of 0.62-0.61, respectively.

CONCLUSION

Integrating synthetic data improves machine learning classifiers' ability to identify health problems in home healthcare, with performance comparable to training on real-world data alone, highlighting the potential of synthetic data in healthcare analytics.

CLINICAL RELEVANCE

This study demonstrates the clinical relevance of leveraging synthetic patient-nurse communication data to enhance machine learning classifier performances to identify health problems in home healthcare settings, which will contribute to more accurate and efficient problem identification and detection of home healthcare patients with complex health conditions.

Utilizing patient-nurse verbal communication in building risk identification models: the missing critical data stream in home healthcare

BACKGROUND

In the United States, over 12 000 home healthcare agencies annually serve 6+ million patients, mostly aged 65+ years with chronic conditions. One in three of these patients end up visiting emergency department (ED) or being hospitalized. Existing risk identification models based on electronic health record (EHR) data have suboptimal performance in detecting these high-risk patients.

OBJECTIVES

To measure the added value of integrating audio-recorded home healthcare patient-nurse verbal communication into a risk identification model built on home healthcare EHR data and clinical notes.

METHODS

This pilot study was conducted at one of the largest not-for-profit home healthcare agencies in the United States. We audio-recorded 126 patient-nurse encounters for 47 patients, out of which 8 patients experienced ED visits and hospitalization. The risk model was developed and tested iteratively using: (1) structured data from the Outcome and Assessment Information Set, (2) clinical notes, and (3) verbal communication features. We used various natural language processing methods to model the communication between patients and nurses.

RESULTS

Using a Support Vector Machine classifier, trained on the most informative features from OASIS, clinical notes, and verbal communication, we achieved an AUC-ROC = 99.68 and an F1-score = 94.12. By integrating verbal communication into the risk models, the F-1 score improved by 26%. The analysis revealed patients at high risk tended to interact more with risk-associated cues, exhibit more "sadness" and "anxiety," and have extended periods of silence during conversation.

CONCLUSION

This innovative study underscores the immense value of incorporating patient-nurse verbal communication in enhancing risk prediction models for hospitalizations and ED visits, suggesting the need for an evolved clinical workflow that integrates routine patient-nurse verbal communication recording into the medical record.

Uncovering hidden trends: identifying time trajectories in risk factors documented in clinical notes and predicting hospitalizations and emergency department visits during home health care

OBJECTIVE

This study aimed to identify temporal risk factor patterns documented in home health care (HHC) clinical notes and examine their association with hospitalizations or emergency department (ED) visits.

MATERIALS AND METHODS

Data for 73 350 episodes of care from one large HHC organization were analyzed using dynamic time warping and hierarchical clustering analysis to identify the temporal patterns of risk factors documented in clinical notes. The Omaha System nursing terminology represented risk factors. First, clinical characteristics were compared between clusters. Next, multivariate logistic regression was used to examine the association between clusters and risk for hospitalizations or ED visits. Omaha System domains corresponding to risk factors were analyzed and described in each cluster.

RESULTS

Six temporal clusters emerged, showing different patterns in how risk factors were documented over time. Patients with a steep increase in documented risk factors over time had a 3 times higher likelihood of hospitalization or ED visit than patients with no documented risk factors. Most risk factors belonged to the physiological domain, and only a few were in the environmental domain.

DISCUSSION

An analysis of risk factor trajectories reflects a patient's evolving health status during a HHC episode. Using standardized nursing terminology, this study provided new insights into the complex temporal dynamics of HHC, which may lead to improved patient outcomes through better treatment and management plans.

CONCLUSION

Incorporating temporal patterns in documented risk factors and their clusters into early warning systems may activate interventions to prevent hospitalizations or ED visits in HHC.

Is the patient speaking or the nurse? Automatic speaker type identification in patient-nurse audio recordings

OBJECTIVES

Patient-clinician communication provides valuable explicit and implicit information that may indicate adverse medical conditions and outcomes. However, practical and analytical approaches for audio-recording and analyzing this data stream remain underexplored. This study aimed to 1) analyze patients' and nurses' speech in audio-recorded verbal communication, and 2) develop machine learning (ML) classifiers to effectively differentiate between patient and nurse language.

MATERIALS AND METHODS

Pilot studies were conducted at VNS Health, the largest not-for-profit home healthcare agency in the United States, to optimize audio-recording patient-nurse interactions. We recorded and transcribed 46 interactions, resulting in 3494 "utterances" that were annotated to identify the speaker. We employed natural language processing techniques to generate linguistic features and built various ML classifiers to distinguish between patient and nurse language at both individual and encounter levels.

RESULTS

A support vector machine classifier trained on selected linguistic features from term frequency-inverse document frequency, Linguistic Inquiry and Word Count, Word2Vec, and Medical Concepts in the Unified Medical Language System achieved the highest performance with an AUC-ROC = 99.01 ± 1.97 and an F1-score = 96.82 ± 4.1. The analysis revealed patients' tendency to use informal language and keywords related to "religion," "home," and "money," while nurses utilized more complex sentences focusing on health-related matters and medical issues and were more likely to ask questions.

CONCLUSION

The methods and analytical approach we developed to differentiate patient and nurse language is an important precursor for downstream tasks that aim to analyze patient speech to identify patients at risk of disease and negative health outcomes.

Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model

OBJECTIVES

Little is known about proactive risk assessment concerning emergency department (ED) visits and hospitalizations in patients with heart failure (HF) who receive home healthcare (HHC) services. This study developed a time series risk model for predicting ED visits and hospitalizations in patients with HF using longitudinal electronic health record data. We also explored which data sources yield the best-performing models over various time windows.

MATERIALS AND METHODS

We used data collected from 9362 patients from a large HHC agency. We iteratively developed risk models using both structured (eg, standard assessment tools, vital signs, visit characteristics) and unstructured data (eg, clinical notes). Seven specific sets of variables included: (1) the Outcome and Assessment Information Set, (2) vital signs, (3) visit characteristics, (4) rule-based natural language processing-derived variables, (5) term frequency-inverse document frequency variables, (6) Bio-Clinical Bidirectional Encoder Representations from Transformers variables, and (7) topic modeling. Risk models were developed for 18 time windows (1-15, 30, 45, and 60 days) before an ED visit or hospitalization. Risk prediction performances were compared using recall, precision, accuracy, F1, and area under the receiver operating curve (AUC).

RESULTS

The best-performing model was built using a combination of all 7 sets of variables and the time window of 4 days before an ED visit or hospitalization (AUC = 0.89 and F1 = 0.69).

DISCUSSION AND CONCLUSION

This prediction model suggests that HHC clinicians can identify patients with HF at risk for visiting the ED or hospitalization within 4 days before the event, allowing for earlier targeted interventions.

Health Information Technology Adoption at U.S. Home Health Care Agencies: Results from a Multi-Methods Study

Health information technology (HIT) holds potential to transform Home Health Care (HHC), yet, little is known about its adoption in this setting. In the context of infection prevention and control, we aimed to: (1) describe challenges associated with the adoption of HIT, for example, electronic health records (EHR) and telehealth and (2) examine HHC agency characteristics associated with HIT adoption. We conducted in-depth interviews with 41 staff from 13 U.S. HHC agencies (May-October 2018), then surveyed a stratified random sample of 1506 agencies (November 2018-December 2019), of which 35.6% participated (N = 536 HHC agencies). We applied analytic weights, generating nationally-representative estimates, and computed descriptive statistics, bivariate and multivariable analyses. Four themes were identified: (1) Reflections on providing HHC without EHR; (2) Benefits of EHR; (3) Benefits of other HIT; (4) Challenges with HIT and EHR. Overall, 10% of the agencies did not have an EHR; an additional 2% were in the process of acquiring one. Sixteen percent offered telehealth, and another 4% were in the process of acquiring telehealth services. In multivariable analysis, EHR use varied significantly by geographic location and ownership, and telehealth use varied by geographic location, ownership, and size. Although HIT use has increased, our results indicate that many HHC agencies still lack the HIT needed to implement technological solutions to improve workflow and quality of care. Future research should examine the impact of HIT on patient outcomes and the impact of the COVID-19 pandemic on HIT use in HHC.

The identification of clusters of risk factors and their association with hospitalizations or emergency department visits in home health care

AIMS

To identify clusters of risk factors in home health care and determine if the clusters are associated with hospitalizations or emergency department visits.

DESIGN

A retrospective cohort study.

METHODS

This study included 61,454 patients pertaining to 79,079 episodes receiving home health care between 2015 and 2017 from one of the largest home health care organizations in the United States. Potential risk factors were extracted from structured data and unstructured clinical notes analysed by natural language processing. A K-means cluster analysis was conducted. Kaplan-Meier analysis was conducted to identify the association between clusters and hospitalizations or emergency department visits during home health care.

RESULTS

A total of 11.6% of home health episodes resulted in hospitalizations or emergency department visits. Risk factors formed three clusters. Cluster 1 is characterized by a combination of risk factors related to "impaired physical comfort with pain," defined as situations where patients may experience increased pain. Cluster 2 is characterized by "high comorbidity burden" defined as multiple comorbidities or other risks for hospitalization (e.g., prior falls). Cluster 3 is characterized by "impaired cognitive/psychological and skin integrity" including dementia or skin ulcer. Compared to Cluster 1, the risk of hospitalizations or emergency department visits increased by 1.95 times for Cluster 2 and by 2.12 times for Cluster 3 (all p < .001).

CONCLUSION

Risk factors were clustered into three types describing distinct characteristics for hospitalizations or emergency department visits. Different combinations of risk factors affected the likelihood of these negative outcomes.

IMPACT

Cluster-based risk prediction models could be integrated into early warning systems to identify patients at risk for hospitalizations or emergency department visits leading to more timely, patient-centred care, ultimately preventing these events.

PATIENT OR PUBLIC CONTRIBUTION

There was no involvement of patients in developing the research question, determining the outcome measures, or implementing the study.

Risk factors for infection in older adults who receive home healthcare and/or home help: A protocol for systematic review and meta-analysis

BACKGROUND

The shift towards home-based care has resulted in increased provision of home healthcare and home help to older adults. Infections acquired in older adults while receiving home care have increased too, resulting in unplanned yet avoidable hospitalizations. In recent years, several studies have reported an array of factors associated with risk of infection; however, no previous systematic review has compiled such evidence, which is important to better protect older adults. Therefore, we have outlined the work of a systematic review that aims to identify risk factors for infection in older adults receiving home healthcare and/or home help.

METHODS

Searches for relevant studies will be conducted in five databases [MEDLINE, EMBASE (Excerpta Medica Database), Web of Science Core Collection, Cinahl (Cumulative Index to Nursing & Allied Health Literature) and Sociological Abstracts]. All types of studies will be included. Exposures considered refer to medical, individual, social/behavioral and environmental risk factors for infection (outcome). Two researchers will independently go through the records generated. Eligible studies will be assessed for risk of biases using the Cochrane risk of bias assessment tool and an overall interpretation of the biases will be provided. If the data allow, a meta-analysis will be conducted. It is possible that both quantitative and qualitative studies will be identified and eligible. Therefore, for the analysis, the Joanna Briggs Institute Reviewers' Manual for mixed methods systematic reviews will be used as it allows for two or more single method reviews (e.g., one quantitative and one qualitative) to be conducted separately and then combined in a joint overarching synthesis.

RESULTS

The findings of the planned systematic review are of interest to healthcare professionals, caregivers, older adults and their families, and policy- and decisions makers in the health and social care sectors as the review will provide evidence-based data on multiple factors that influence the risk of infection among older adults receiving care in their homes.

CONCLUSION

The results could guide future policy on effective infection control in the home care sector.

Audio Recording Patient-Nurse Verbal Communications in Home Health Care Settings: Pilot Feasibility and Usability Study

Background

Patients’ spontaneous speech can act as a biomarker for identifying pathological entities, such as mental illness. Despite this potential, audio recording patients’ spontaneous speech is not part of clinical workflows, and health care organizations often do not have dedicated policies regarding the audio recording of clinical encounters. No previous studies have investigated the best practical approach for integrating audio recording of patient-clinician encounters into clinical workflows, particularly in the home health care (HHC) setting.

Objective

This study aimed to evaluate the functionality and usability of several audio-recording devices for the audio recording of patient-nurse verbal communications in the HHC settings and elicit HHC stakeholder (patients and nurses) perspectives about the facilitators of and barriers to integrating audio recordings into clinical workflows.

Methods

This study was conducted at a large urban HHC agency located in New York, United States. We evaluated the usability and functionality of 7 audio-recording devices in a laboratory (controlled) setting. A total of 3 devices—Saramonic Blink500, Sony ICD-TX6, and Black Vox 365—were further evaluated in a clinical setting (patients’ homes) by HHC nurses who completed the System Usability Scale questionnaire and participated in a short, structured interview to elicit feedback about each device. We also evaluated the accuracy of the automatic transcription of audio-recorded encounters for the 3 devices using the Amazon Web Service Transcribe. Word error rate was used to measure the accuracy of automated speech transcription. To understand the facilitators of and barriers to integrating audio recording of encounters into clinical workflows, we conducted semistructured interviews with 3 HHC nurses and 10 HHC patients. Thematic analysis was used to analyze the transcribed interviews.

Results

Saramonic Blink500 received the best overall evaluation score. The System Usability Scale score and word error rate for Saramonic Blink500 were 65% and 26%, respectively, and nurses found it easier to approach patients using this device than with the other 2 devices. Overall, patients found the process of audio recording to be satisfactory and convenient, with minimal impact on their communication with nurses. Although, in general, nurses also found the process easy to learn and satisfactory, they suggested that the audio recording of HHC encounters can affect their communication patterns. In addition, nurses were not aware of the potential to use audio-recorded encounters to improve health care services. Nurses also indicated that they would need to involve their managers to determine how audio recordings could be integrated into their clinical workflows and for any ongoing use of audio recordings during patient care management.

Conclusions

This study established the feasibility of audio recording HHC patient-nurse encounters. Training HHC nurses about the importance of the audio-recording process and the support of clinical managers are essential factors for successful implementation.

Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care

BACKGROUND/OBJECTIVE

Between 10 and 25% patients are hospitalized or visit emergency department (ED) during home healthcare (HHC). Given that up to 40% of these negative clinical outcomes are preventable, early and accurate prediction of hospitalization risk can be one strategy to prevent them. In recent years, machine learning-based predictive modeling has become widely used for building risk models. This study aimed to compare the predictive performance of four risk models built with various data sources for hospitalization and ED visits in HHC.

METHODS

Four risk models were built using different variables from two data sources: structured data (i.e., Outcome and Assessment Information Set (OASIS) and other assessment items from the electronic health record (EHR)) and unstructured narrative-free text clinical notes for patients who received HHC services from the largest non-profit HHC organization in New York between 2015 and 2017. Then, five machine learning algorithms (logistic regression, Random Forest, Bayesian network, support vector machine (SVM), and Naïve Bayes) were used on each risk model. Risk model performance was evaluated using the F-score and Precision-Recall Curve (PRC) area metrics.

RESULTS

During the study period, 8373/86,823 (9.6%) HHC episodes resulted in hospitalization or ED visits. Among five machine learning algorithms on each model, the SVM showed the highest F-score (0.82), while the Random Forest showed the highest PRC area (0.864). Adding information extracted from clinical notes significantly improved the risk prediction ability by up to 16.6% in F-score and 17.8% in PRC.

CONCLUSION

All models showed relatively good hospitalization or ED visit risk predictive performance in HHC. Information from clinical notes integrated with the structured data improved the ability to identify patients at risk for these emergent care events.

Exploring infection prevention practices in home-based nursing care: A qualitative observational study

BACKGROUND

Home-based nursing care continues to expand, delivering care to increasingly older clients with multiple, chronic and complex conditions that require the use of additional and more numerous invasive medical devices. Therefore, the prevention of infections poses a challenge for nurses, professional caregivers and clients.

OBJECTIVE

This article explores infection prevention practices and related behavioural factors in both nurses and clients to identify barriers and facilitators of infection prevention practices in home-based nursing care.

DESIGN

A qualitative, exploratory design.

SETTING

Four healthcare organisations providing home-based nursing care in the Netherlands.

METHODS

Participant observations were used as the main source of data collection complemented with focus group discussions and semi-structured interviews.

PARTICIPANTS

Participant observations: 16 nurses, three professional caregivers and 80 clients. Semi-structured interviews: 11 clients. Focus group discussions: 15 nurses and four professional caregivers.

RESULTS

A total of 87 unique care delivery situations were observed for 55 h, complemented with three focus group discussions and 11 individual semi-structured client interviews. Infection prevention practices in home-based nursing care appeared to be challenged by 1. The specific context or environment in which the care occurred, which is more autonomous, less structured, less controlled and less predictable than other care settings; 2. Suboptimal and considerable variation in professional performance concerning the application of hand hygiene and the proper use of personal protective equipment such as face masks, barrier gowns and disposable gloves; 3. Extensive use in and outside the client's surroundings of communication devices that are irregularly cleaned and tend to interrupt nursing procedures; and 4. Inadequate organisational support in the implementation and evaluation of new information or policy changes and fragmentation, variation and conflicting information regarding professional guidelines and protocols.

CONCLUSIONS

From a first-hand observational viewpoint, this study showed that the daily practice of infection prevention in home-based nursing care appears to be suboptimal. Furthermore, this research revealed considerable variation in the work environment, the application of hand hygiene, the proper use of personal protective equipment, the handling of communication devices and organisational policies, procedures and support. Finally, the study identified a number of important barriers and facilitators of infection prevention practices in the work environment, professional and team performance, clients and organisations.

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.

Observation of Hand Hygiene Practices in Home Health Care

OBJECTIVE

To describe nurse hand hygiene practices in the home health care (HHC) setting, nurse adherence to hand hygiene guidelines, and factors associated with hand hygiene opportunities during home care visits.

DESIGN

Observational study of nurse hand hygiene practices.

SETTING

and Participants: Licensed practical/vocational and registered nurses were observed in the homes of patients being served by a large nonprofit HHC agency.

METHODS

Two researchers observed 400 home care visits conducted by 50 nurses. The World Health Organization's "5 Moments for Hand Hygiene" validated observation tool was used to record opportunities and actual practices of hand hygiene, with 3 additional opportunities specific to the HHC setting. Patient assessment data available in the agency electronic health record and a nurse demographic questionnaire were also collected to describe patients and nurse participants.

RESULTS

A total of 2014 opportunities were observed. On arrival in the home was the most frequent opportunity (n = 384), the least frequent was after touching a patient's surroundings (n = 43). The average hand hygiene adherence rate was 45.6% after adjusting for clustering at the nurse level. Adherence was highest after contact with body fluid (65.1%) and lowest after touching a patient (29.5%). The number of hand hygiene opportunities was higher when patients being served were at increased risk of an infection-related emergency department visit or hospitalization and when the home environment was observed to be "dirty." No nurse or patient demographic characteristics were associated with the rate of nurse hand hygiene adherence.

CONCLUSIONS AND IMPLICATIONS

Hand hygiene adherence in HHC is suboptimal, with rates mirroring those reported in hospital and outpatient settings. The connection between poor hand hygiene and infection transmission has been well studied, and it has received widespread attention with the outbreak of SARS-CoV-2. Agencies can use results found in this study to better inform quality improvement initiatives.

Individual, social, and environmental factors for infection risk among home healthcare patients: A multi-method study

There has been limited research into the individual, social, and environmental factors for infection risk among patients in the home healthcare (HHC) setting, where the infection is a leading cause of hospitalisation. The aims of this study were to (1) explore nurse perceptions of individual, social, and environmental factors for infection risk among HHC patients; and (2) identify the frequency of environmental barriers to infection prevention and control in HHC. Data were collected in 2017-2018 and included qualitative interviews with HHC nurses (n = 50) and structured observations of nurse visits to patients' homes (n = 400). Thematic analyses of interviews with nurses suggested they perceived infection risk among patients as being influenced by knowledge of and attitudes towards infection prevention and engagement in hygiene practices, receipt of support from informal caregivers and nurse interventions aimed at cultivating infection control knowledge and practices, and the home environment. Statistical analyses of observation checklists revealed nurses encountered an average of 1.7 environmental barriers upon each home visit. Frequent environmental barriers observed during visits to HHC patients included clutter (39.5%), poor lighting (38.8%), dirtiness (28.5%), and pets (17.2%). Additional research is needed to clarify inter-relationships among these factors and identify strategies for addressing each as part of a comprehensive infection control program in HHC.

Surviving COVID-19 After Hospital Discharge: Symptom, Functional, and Adverse Outcomes of Home Health Recipients

BACKGROUND

Little is known about recovery from coronavirus disease 2019 (COVID-19) after hospital discharge.

OBJECTIVE

To describe the home health recovery of patients with COVID-19 and risk factors associated with rehospitalization or death.

DESIGN

Retrospective observational cohort.

SETTING

New York City.

PARTICIPANTS

1409 patients with COVID-19 admitted to home health care (HHC) between 1 April and 15 June 2020 after hospitalization.

MEASUREMENTS

Covariates and outcomes were obtained from the mandated OASIS (Outcome and Assessment Information Set). Cox proportional hazards models were used to estimate the hazard ratio (HR) of risk factors associated with rehospitalization or death.

RESULTS

After an average of 32 days in HHC, 94% of patients were discharged and most achieved statistically significant improvements in symptoms and function. Activity-of-daily-living dependencies decreased from an average of 6 (95% CI, 5.9 to 6.1) to 1.2 (CI, 1.1 to 1.3). Risk for rehospitalization or death was higher for male patients (HR, 1.45 [CI, 1.04 to 2.03]); White patients (HR, 1.74 [CI, 1.22 to 2.47]); and patients with heart failure (HR, 2.12 [CI, 1.41 to 3.19]), diabetes with complications (HR, 1.71 [CI, 1.17 to 2.52]), 2 or more emergency department visits in the past 6 months (HR, 1.78 [CI, 1.21 to 2.62]), pain daily or all the time (HR, 1.46 [CI, 1.05 to 2.05]), cognitive impairment (HR, 1.49 [CI, 1.04 to 2.13]), or functional dependencies (HR, 1.09 [CI, 1.00 to 1.20]). Eleven patients (1%) died, 137 (10%) were rehospitalized, and 23 (2%) remain on service.

LIMITATIONS

Care was provided by 1 home health agency. Information on rehospitalization and death after HHC discharge is not available.

CONCLUSION

Symptom burden and functional dependence were common at the time of HHC admission but improved for most patients. Comorbid conditions of heart failure and diabetes, as well as characteristics present at admission, identified patients at greatest risk for an adverse event.

PRIMARY FUNDING SOURCE

No direct funding.

"A catalyst for action": Factors for implementing clinical risk prediction models of infection in home care settings

OBJECTIVE

The study sought to outline how a clinical risk prediction model for identifying patients at risk of infection is perceived by home care nurses, and to inform how the output of the model could be integrated into a clinical workflow.

MATERIALS AND METHODS

This was a qualitative study using semi-structured interviews with 50 home care nurses. Interviews explored nurses' perceptions of clinical risk prediction models, their experiences using them in practice, and what elements are important for the implementation of a clinical risk prediction model focusing on infection. Interviews were audio-taped and transcribed, with data evaluated using thematic analysis.

RESULTS

Two themes were derived from the data: (1) informing nursing practice, which outlined how a clinical risk prediction model could inform nurse clinical judgment and be used to modify their care plan interventions, and (2) operationalizing the score, which summarized how the clinical risk prediction model could be incorporated in home care settings.

DISCUSSION

The findings indicate that home care nurses would find a clinical risk prediction model for infection useful, as long as it provided both context around the reasons why a patient was deemed to be at high risk and provided some guidance for action.

CONCLUSIONS

It is important to evaluate the potential feasibility and acceptability of a clinical risk prediction model, to inform the intervention design and implementation strategy. The results of this study can provide guidance for the development of the clinical risk prediction tool as an intervention for integration in home care settings.

LACE Score-Based Risk Management Tool for Long-Term Home Care Patients: A Proof-of-Concept Study in Taiwan

Background: Effectively predicting and reducing readmission in long-term home care (LTHC) is challenging. We proposed, validated, and evaluated a risk management tool that stratifies LTHC patients by LACE predictive score for readmission risk, which can further help home care providers intervene with individualized preventive plans. Method: A before-and-after study was conducted by a LTHC unit in Taiwan. Patients with acute hospitalization within 30 days after discharge in the unit were enrolled as two cohorts (Pre-Implement cohort in 2017 and Post-Implement cohort in 2019). LACE score performance was evaluated by calibration and discrimination (AUC, area under receiver operator characteristic (ROC) curve). The clinical utility was evaluated by negative predictive value (NPV). Results: There were 48 patients with 87 acute hospitalizations in Pre-Implement cohort, and 132 patients with 179 hospitalizations in Post-Implement cohort. These LTHC patients were of older age, mostly intubated, and had more comorbidities. There was a significant reduction in readmission rate by 44.7% (readmission rate 25.3% vs. 14.0% in both cohorts). Although LACE score predictive model still has room for improvement (AUC = 0.598), it showed the potential as a useful screening tool (NPV, 87.9%; 95% C.I., 74.2–94.8). The reduction effect is more pronounced in infection-related readmission. Conclusion: As real-world evidence, LACE score-based risk management tool significantly reduced readmission by 44.7% in this LTHC unit. Larger scale studies involving multiple homecare units are needed to assess the generalizability of this study.