Agreement between patient-reported and clinically documented symptoms of acute myeloid leukemia: Study protocol.

AIM: To describe our methods to compare patient-reported symptoms of acute myeloid leukemia and the corresponding documentation by healthcare providers in the electronic health record. BACKGROUND: Patients with acute myeloid leukemia experience many distressing symptoms, particularly related to chemotherapy. The timely recognition and provision of evidence-based interventions to manage these symptoms can improve outcomes. However, lack of standardized formatting for symptom documentation within electronic health records leads to challenges for clinicians when accessing and comprehending patients' symptom information, as it primarily exists in narrative forms in various parts of the electronic health record. This variability raises concerns about over- or under-reporting of symptoms. Consistency between patient-reported symptoms and clinician's symptom documentation is important for patient-centered symptom management, but little is known about the degree of agreement between patient reports and their documentation. This is a detailed description of the study's methodology, procedures and design to determine how patient-reported symptoms are similar or different from symptoms documented in electronic health records by clinicians. DESIGN: Exploratory, descriptive study. METHODS: Forty symptoms will be assessed as patient-reported outcomes using the modified version of the Memorial Symptom Assessment Scale. The research team will annotate symptoms from the electronic health record (clinical notes and flowsheets) corresponding to the 40 symptoms. The degree of agreement between patient reports and electronic health record documentation will be analyzed using positive and negative agreement, kappa statistics and McNemar's test. CONCLUSION: We present innovative methods to comprehensively compare the symptoms reported by acute myeloid leukemia patients with all available electronic health record documentation, including clinical notes and flowsheets, providing insights into symptom reporting in clinical practice. IMPACT: Findings from this study will provide foundational understanding and compelling evidence, suggesting the need for more thorough efforts to assess patients' symptoms. Methods presented in this paper are applicable to other symptom-intensive diseases.

Development and Testing of the Relational and Structural Components of Innovativeness Across Academia and Practice for Healthcare Progress Scale.

OBJECTIVE: Using data from 5 academic-practice sites across the United States, researchers developed and validated a scale to measure conditions that enable healthcare innovations. BACKGROUND: Academic-practice partnerships are a catalyst for innovation and healthcare development. However, limited theoretically grounded evidence exists to provide strategic direction for healthcare innovation across practice and academia. METHODS: Phase 1 of the analytical strategy involved scale development using 16 subject matter experts. Phase 2 involved pilot testing the scale. RESULTS: The final Innovativeness Across Academia and Practice for Healthcare Progress Scale (IA-APHPS) consisted of 7 domains: 3 relational domains, 2 structural domains, and 2 impact domains. The confirmatory factor analysis model fits well with a comparative fit index of 0.92 and a root-mean-square error of approximation of 0.06 (n = 477). CONCLUSION: As the 1st validated scale of healthcare innovation, the IA-APHPS allows nurses to use a diagnostic tool to facilitate innovative processes and outputs across academic-practice partnerships.

Linking nursing outcomes classification to the self- and family management framework.

AIM: Establish linkages between components of the Self- and Family Management Framework and outcomes of the Nursing Outcomes Classification to evaluate the comprehensiveness of outcomes addressing self- and family management in the Nursing Outcomes Classification. DESIGN: Descriptive study. METHODS: Experts conducted a six-step process to establish linkages: (1) preliminary mapping of all relevant nursing outcomes to the framework; (2) development of checklists for team members serving as 'identifiers' and 'reviewers'; (3) mapping all relevant nursing outcomes to the framework; (4) final agreement on mapped outcomes; (5) establishment of inter-rater reliability; and (6) discussion of findings with authors of the Self- and Family Management Framework. RESULTS: Three hundred and sixty-three nursing outcomes were identified as related to the management of chronic disease across all components of the framework: outcomes related to patient self-management (n = 336), family functioning (n = 16) and family caregivers (n = 11). CONCLUSION: The Nursing Outcomes Classification outcomes comprehensively address self-management, and, less so, family functioning, and caregivers. IMPLICATIONS: Established linkages can be used by nurses to track and support patient and family management outcomes across the care continuum. PATIENT OR PUBLIC CONTRIBUTION: Linking standardized nursing outcomes to the Self- and Family Management Framework can assist in goal setting and measurement of nursing care during chronic disease management. This work can help describe to funders, policy makers and others invested in health care reform the specific contributions of nurses to self- and family management of chronic disease. IMPACT: This paper demonstrates the linkages between components of the Self- and Family Management Framework and Nursing Outcomes Classification outcomes. The results of this study offer the opportunity to quantify the impact of nursing care and enhance nursing practice for patients with chronic conditions as well as contribute to developing Nursing Outcomes Classification outcomes that consider self-management processes.

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.

Validation of Nursing Outcomes Classification: Knowledge and Self-management for Cardiac Disease.

The Nursing Outcomes Classification provides two outcomes, Knowledge: Cardiac Disease and Self-management: Cardiac Disease, to assess knowledge and self-management behaviors of adults with cardiac disease. The purpose of this study was to validate the two nursing-sensitive outcomes to establish content validity. A methodological design was used using the Delphi technique. A total of 13 nurse experts in two domains participated in this study: five in standardized nursing terminologies and eight in self-management. Descriptive statistics and the Nurse-Patient Outcome Content Validity method were used to validate four aspects: definition adequacy of each outcome, clinical usefulness of measurement scales, importance of outcome indicators, and content similarity between the two outcomes. The definition adequacy, clinical usefulness, and content similarity of both outcomes were acceptable. A total of 81 indicators from the two outcomes were validated, and 60 were designated as critical. Nurses can evaluate cardiac patient outcomes effectively and accurately using these validated outcomes. The validated Nursing Outcomes Classification outcomes will also support the clinical decision-making of nursing students when they learn about patients with cardiac disease.

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.

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.

Improved readability and functions needed for mHealth apps targeting patients with heart failure: An app store review.

To maintain their quality of life and avoid hospitalization and early mortality, patients with heart failure must recognize and respond to symptoms of exacerbation. A promising method for engaging patients in their self-care is through mobile health applications (mHealth apps). However, for mHealth to have its greatest chance for improving patient outcomes, the app content must be readable, provide useful functions and be based in evidence. The study aimed to determine: (1) readability, (2) types of functions, and (3) linkage to authoritative sources of evidence for self-care focused mHealth apps targeting heart failure patients that are available in the Apple and Google Play Stores. We systematically searched for mHealth apps targeting patients with heart failure in the Apple and Google Play Stores and applied selection criteria. Readability of randomly selected informational paragraphs were determined using Flesch-Kincaid grade level test tool in Microsoft Word. Ten mHealth apps met our criteria. Only one had a reading grade level at or below the recommended 6th grade reading level (average 9.35). The most common functions were tracking, clinical data feedback, and non-data-based reminders and alerts. Only three had statements that clearly linked the mHealth app content to trustworthy, evidence-based sources. Only two had interoperability with the electronic health record and only one had a communication feature with clinicians. Future mHealth designs that are tailored to patients' literacy level and have advanced functions may hold greater potential for improving patient outcomes.

Warming of Irrigation Fluids for Prevention of Perioperative Hypothermia During Arthroscopy: A Systematic Review and Meta-analysis.

PURPOSE: To determine whether warming of irrigation fluids (32°C-40°C) compared with using room-temperature irrigation fluids (20°C-22°C) decreases the risk of perioperative hypothermia (<36°C) for patients undergoing shoulder, hip, or knee arthroscopy. METHODS: One reviewer, with the assistance of a medical librarian, searched the following databases: PubMed, Embase, Cochrane Central, SPORTDiscus, Web of Science, and CINAHL (Cumulative Index to Nursing and Allied Health Literature). Level I and II studies involving shoulder, hip, or knee arthroscopy were included. Two reviewers screened the abstracts and titles. Two reviewers assessed the risk of bias of selected studies using The Cochrane Collaboration tool. Meta-analyses were conducted on the following outcomes: hypothermia, lowest temperature, maximum temperature drop, and shivering. RESULTS: Seven studies of patients undergoing arthroscopy were included in the qualitative synthesis (5 shoulder studies, 1 hip study, and 1 knee study; 501 patients). The study involving knee arthroscopy was excluded from the meta-analyses because of insufficient data and high clinical heterogeneity (surgical site distal to the core, not involving extravasation of large amounts of fluid). The remaining 6 studies were included in 1 or more meta-analyses: hypothermia (5 shoulder and 1 hip study), lowest temperature (3 shoulder and 1 hip study), maximum temperature drop (2 shoulder and 1 hip study), and shivering (5 shoulder and 1 hip study). Warming of irrigation fluids for shoulder or hip arthroscopy significantly decreased the risk of hypothermia (odds ratio, 0.15; 95% confidence interval [CI], 0.06-0.40; P = .0001), increased the lowest mean temperature (mean difference, 0.46°C; 95% CI, 0.11°C-0.81°C; P = .01), decreased the maximum temperature drop (mean difference, -0.64°C; 95% CI, -0.94°C to -0.35°C; P < .0001), and decreased the risk of shivering (odds ratio, 0.25; 95% CI, 0.07-0.86; P = .03). CONCLUSIONS: When irrigation fluids are warmed for shoulder and hip arthroscopy, the risk of hypothermia is less, the drop in intraoperative temperature is less, the lowest body temperature is higher, and the risk of postoperative shivering is reduced. LEVEL OF EVIDENCE: Level II, systematic review of Level I and II studies.

Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network.

PURPOSE: Ability to predict symptom severity and progression across treatment trajectories would allow clinicians to provide timely intervention and treatment planning. However, such predictions are difficult because of sparse and inconsistent assessment, and simplistic measures such as the last observed symptom severity are often used. The purpose of this study is to develop a model for predicting future cancer symptom experiences on the basis of past symptom experiences. PATIENTS AND METHODS: We performed a retrospective, longitudinal analysis using records of patients with cancer (n = 208) hospitalized between 2008 and 2014. A long short-term memory (LSTM)-based recurrent neural network, a linear regression, and random forest models were trained on previous symptoms experienced and used to predict future symptom trajectories. RESULTS: We found that at least one of three tested models (LSTM, linear regression, and random forest) outperform predictions based solely on the previous clinical observation. LSTM models significantly outperformed linear regression and random forest models in predicting nausea (P < .1) and psychosocial status (P < .01). Linear regression outperformed all models when predicting oral health (P < .01), while random forest outperformed all models when predicting mobility (P < .01) and nutrition (P < .01). CONCLUSION: We can successfully predict patients' symptom trajectories with a prediction model, built with sparse assessment data, using routinely collected nursing documentation. The results of this project can be applied to better individualize symptom management to support cancer patients' quality of life.

Fairness gaps in Machine learning models for hospitalization and emergency department visit risk prediction in home healthcare patients with heart failure.

OBJECTIVES: This study aims to evaluate the fairness performance metrics of Machine Learning (ML) models to predict hospitalization and emergency department (ED) visits in heart failure patients receiving home healthcare. We analyze biases, assess performance disparities, and propose solutions to improve model performance in diverse subpopulations. METHODS: The study used a dataset of 12,189 episodes of home healthcare collected between 2015 and 2017, including structured (e.g., standard assessment tool) and unstructured data (i.e., clinical notes). ML risk prediction models, including Light Gradient-boosting model (LightGBM) and AutoGluon, were developed using demographic information, vital signs, comorbidities, service utilization data, and the area deprivation index (ADI) associated with the patient's home address. Fairness metrics, such as Equal Opportunity, Predictive Equality, Predictive Parity, and Statistical Parity, were calculated to evaluate model performance across subpopulations. RESULTS: Our study revealed significant disparities in model performance across diverse demographic subgroups. For example, the Hispanic, Male, High-ADI subgroup excelled in terms of Equal Opportunity with a metric value of 0.825, which was 28% higher than the lowest-performing Other, Female, Low-ADI subgroup, which scored 0.644. In Predictive Parity, the gap between the highest and lowest-performing groups was 29%, and in Statistical Parity, the gap reached 69%. In Predictive Equality, the difference was 45%. DISCUSSION AND CONCLUSION: The findings highlight substantial differences in fairness metrics across diverse patient subpopulations in ML risk prediction models for heart failure patients receiving home healthcare services. Ongoing monitoring and improvement of fairness metrics are essential to mitigate biases.

Home Healthcare Patients With Distinct Psychological, Cognitive, and Behavioral Symptom Profiles and At-Risk Subgroup for Hospitalization and Emergency Department Visits Using Latent Class Analysis.

One-third of home healthcare patients are hospitalized or visit emergency departments during a 60-day episode of care. Among all risk factors, psychological, cognitive, and behavioral symptoms often remain underdiagnosed or undertreated in older adults. Little is known on subgroups of older adults receiving home healthcare services with similar psychological, cognitive, and behavioral symptom profiles and an at-risk subgroup for future hospitalization and emergency department visits. Our cross-sectional study used data from a large, urban home healthcare organization (n = 87,943). Latent class analysis was conducted to identify meaningful subgroups of older adults based on their distinct psychological, cognitive, and behavioral symptom profiles. Adjusted multiple logistic regression was used to understand the association between the latent subgroup and future hospitalization and emergency department visits. Descriptive and inferential statistics were conducted to describe the individual characteristics and to test for significant differences. The three-class model consisted of Class 1: "Moderate psychological symptoms without behavioral issues," Class 2: "Severe psychological symptoms with behavioral issues," and Class 3: "Mild psychological symptoms without behavioral issues." Compared to Class 3, Class 1 patients had 1.14 higher odds and Class 2 patients had 1.26 higher odds of being hospitalized or visiting emergency departments. Significant differences were found in individual characteristics such as age, gender, race/ethnicity, and insurance. Home healthcare clinicians should consider the different latent subgroups of older adults based on their psychological, cognitive, and behavioral symptoms. In addition, they should provide timely assessment and intervention especially to those at-risk for hospitalization and emergency department visits.

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.

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.

Longitudinal Subgrouping of Patients With Cancer Based on Symptom Experiences: An Integrative Review.

PROBLEM IDENTIFICATION: The purpose of this integrative review is to identify literature describing (a) subgrouping patients with cancer based on symptom experiences and their patterns of symptom changes over time and (b) methodologies of subgrouping patients with cancer based on symptom experiences. LITERATURE SEARCH: PubMed®, CINAHL®, and PsycINFO® were searched through January 2019. DATA EVALUATION: Studies were appraised for patterns of symptom change over time and methodologic approach using the QualSyst quality rating scale. SYNTHESIS: 11 studies met inclusion criteria. Clinical variables that influence symptom patterns were diverse. The most common clustering method was latent variable analysis (73%), and all studies collected symptom data prospectively using survey analysis to assess symptoms. IMPLICATIONS FOR PRACTICE: The majority of studies (91%) observed that the symptom experience within the group of patients with cancer changed over time. Identifying groups of patients with similar symptom experiences is useful to determine which patients need more intensive symptom management over the trajectory of cancer treatment, which is essential to improve symptoms and quality of life.

Capturing Concerns about Patient Deterioration in Narrative Documentation in Home Healthcare.

Home healthcare (HHC) agencies provide care to more than 3.4 million adults per year. There is value in studying HHC narrative notes to identify patients at risk for deterioration. This study aimed to build machine learning algorithms to identify "concerning" narrative notes of HHC patients and identify emerging themes. Six algorithms were applied to narrative notes (n = 4,000) from a HHC agency to classify notes as either "concerning" or "not concerning." Topic modeling using Latent Dirichlet Allocation bag of words was conducted to identify emerging themes from the concerning notes. Gradient Boosted Trees demonstrated the best performance with a F-score = 0.74 and AUC = 0.96. Emerging themes were related to patient-clinician communication, HHC services provided, gait challenges, mobility concerns, wounds, and caregivers. Most themes have been cited by previous literature as increasing risk for adverse events. In the future, such algorithms can support early identification of patients at risk for deterioration.

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.

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.

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.

Are We Getting What We Really Want? A Systematic Review of Concordance Between Physician Orders for Life-Sustaining Treatment (POLST) Documentation and Subsequent Care Delivered at End-of-Life.

BACKGROUND: Physician Orders for Life-Sustaining Treatments (POLST) is an advance care planning (ACP) tool that is designed to facilitate End-of-Life (EoL) care discussions between a medical provider and a terminally ill patient. It is often used as a tool to translate care wishes into a medical order, which can be honored across healthcare settings. With an increased utilization of the POLST paradigm in various healthcare settings along with continued dissemination across the nation, it is critical to examine whether documented wishes on POLST are concordant with subsequent care delivered. Purpose of this article was to examine concordance rate between POLST and subsequent care delivered in any care settings and communities. DESIGN: Systematic review. RESULTS: Of 1,406 articles identified, 10 articles met inclusion criteria. Together, included studies represent 5,688 POLST forms reviewed from individuals residing in a total of 126 nursing care facilities, 9 elderly care centers, 4 community settings, and 2 hospitals. Preference for cardiopulmonary resuscitation and actual delivery/ withholding of resuscitation was the most observed intervention in study of concordance (n = 8). It is also where highest concordance rate (97.5%) was reported. Seven studies compared care provided during EoL and the level of medical intervention requested on POLST forms (91.17% concordance). Preference to use artificial nutrition/ hydration, and actual delivery was 93.0% (n = 4 studies), and antibiotics use preference and delivery was 96.5% (reported in 4 studies). CONCLUSION: Published literature evidence suggests that EoL care wishes documented on POLST forms were largely concordant with subsequent care delivered. Additional research is needed to evaluate concordance between POLST documentation and care received among POLST users, who experienced multiple care transitions across healthcare settings, or across state during EoL care journey.