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.

Voices Unheard: Exploring New Data Sources in Nursing Through Speech Processing.

The complex nature of verbal patient-nurse communication holds valuable insights for nursing research, but traditional documentation methods often miss these crucial details. This study explores the emerging role of speech processing technology in nursing research, emphasizing patient-nurse verbal communication. We conducted case studies across various healthcare settings, revealing a substantial gap in electronic health records for capturing vital patient-nurse encounters. Our research demonstrates that speech processing technology can effectively bridge this gap, enhancing documentation accuracy and enriching data for quality care assessment and risk prediction. The technology's application in home healthcare, outpatient settings, and specialized areas like dementia care illustrates its versatility. It offers the potential for real-time decision support, improved communication training, and enhanced telehealth practices. This paper provides insights into the promises and challenges of integrating speech processing into nursing practice, paving the way for future patient care and healthcare data management advancements.

Future Needs in Nursing Informatics - Preliminary Findings from the Global Nursing Informatics Survey.

This study investigates the evolving landscape of nursing informatics by conducting a follow-up survey initiated by the International Medical Informatics Association (IMIA) Students and Emerging Professionals (SEP) Nursing Informatics (NI) group in 2015 and 2019. The participants were asked to describe what they thought should be done in their institutions and countries to advance nursing informatics in the next 5-10 years. For this paper, responses in English acquired by December 2023 were analysed using inductive content analysis. Identified needs covered a) recognition and roles, b) educational needs, c) technological needs, and d) research needs. The initial findings indicate that, despite significant progress in nursing informatics, the current needs closely mirror those identified in the 2015 survey.

EHR Solutions for Information Transfer Deficits During Transitions in Care for Sepsis Survivors.

In a previous study, sepsis was noted as a diagnosis on the home health record only 4% of the time for 165,000 sepsis survivors transitioning from hospital to home health care in America. If sepsis and other conditions are not clearly documented in the transitional care record this can lead to unpreparedness, missed, care, and poor patient outcomes. Our implementation science study discovered a source of this problem regarding the sepsis documentation in 16 hospitals referring to five home care agencies. Together, researchers, hospital, and home care personnel developed and implemented two information technology solutions to address this deficit in seven hospitals. The automated method was more readily adopted and effective in improving information transfer between hospital and home health care.

The Role of Health Information Technology During Hospital to Home Transitions.

The use of healthcare information technology (HIT) is vital for storing and exchanging health information during patient transitions, playing a significant role in care coordination for sepsis survivors. The critical role of HIT was evident during the pre-implementation phase of a study to implement an evidence-based protocol supporting the timely transition of sepsis survivors to home health and outpatient care. Through 61 semi-structured interviews involving 91 stakeholders, over half of the 33 identified themes were related to HIT. Notably, electronic health record (EHR) alert systems led to over-capture and alarm fatigue. Efficient information transfer during HHC referral highlighted the need for improved EHR access. The study underscores HIT's importance and potential while emphasizing the need for collaborative policy and interface development to promote effective transitions in care.

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.

Social Determinants Documented Across Individuals from Different Racial and Ethnic Groups in Home Healthcare.

Home healthcare (HHC) enables patients to receive health services within their homes. Social determinants of health (SDOH) influence a patient's health and may disproportionately affect patients from racially and ethnically minoritized groups. This study describes differences in SDOH documentation in clinical notes among individuals from different racial or ethnic groups from one HHC agency in the northeastern United States. Compared to White patients, HHC episodes for patients across racially and ethnically minoritized groups had higher frequencies of SDOH documented. Further, our results suggest that race or ethnicity is significantly associated with SDOH documentation.

Exploring the full potential of the electronic health record: the application of natural language processing for clinical practice.

The electronic health record contains valuable patient data and offers opportunities to administer and analyze patients' individual needs longitudinally. However, most information in the electronic health record is currently stored in unstructured text notations. Natural Language Processing (NLP), a branch of artificial intelligence that enables computers to understand, interpret, and generate human language, can be used to delve into unstructured text data to uncover valuable insights and knowledge. This article discusses different types of NLP, the potential of NLP for cardiovascular nursing, and how to get started with NLP as a clinician.

Documentation of social determinants of health across individuals from different racial and ethnic groups in home healthcare.

INTRODUCTION: Home healthcare (HHC) enables patients to receive healthcare services within their homes to manage chronic conditions and recover from illnesses. Recent research has identified disparities in HHC based on race or ethnicity. Social determinants of health (SDOH) describe the external factors influencing a patient's health, such as access to care and social support. Individuals from racially or ethnically minoritized communities are known to be disproportionately affected by SDOH. Existing evidence suggests that SDOH are documented in clinical notes. However, no prior study has investigated the documentation of SDOH across individuals from different racial or ethnic backgrounds in the HHC setting. This study aimed to (1) describe frequencies of SDOH documented in clinical notes by race or ethnicity and (2) determine associations between race or ethnicity and SDOH documentation. DESIGN: Retrospective data analysis. METHODS: We conducted a cross-sectional secondary data analysis of 86,866 HHC episodes representing 65,693 unique patients from one large HHC agency in New York collected between January 1, 2015, and December 31, 2017. We reported the frequency of six SDOH (physical environment, social environment, housing and economic circumstances, food insecurity, access to care, and education and literacy) documented in clinical notes across individuals reported as Asian/Pacific Islander, Black, Hispanic, multi-racial, Native American, or White. We analyzed differences in SDOH documentation by race or ethnicity using logistic regression models. RESULTS: Compared to patients reported as White, patients across other racial or ethnic groups had higher frequencies of SDOH documented in their clinical notes. Our results suggest that race or ethnicity is associated with SDOH documentation in HHC. CONCLUSION: As the study of SDOH in HHC continues to evolve, our results provide a foundation to evaluate social information in the HHC setting and understand how it influences the quality of care provided. CLINICAL RELEVANCE: The results of this exploratory study can help clinicians understand the differences in SDOH across individuals from different racial and ethnic groups and serve as a foundation for future research aimed at fostering more inclusive HHC documentation practices.

Natural Language Processing to Identify Home Health Care Patients at Risk for Becoming Incapacitated With No Evident Advance Directives or Surrogates.

OBJECTIVES: Home health care patients who are at risk for becoming Incapacitated with No Evident Advance Directives or Surrogates (INEADS) may benefit from timely intervention to assist them with advance care planning. This study aimed to develop natural language processing algorithms for identifying home care patients who do not have advance directives, family members, or close social contacts who can serve as surrogate decision-makers in the event that they lose decisional capacity. DESIGN: Cross-sectional study of electronic health records. SETTING AND PARTICIPANTS: Patients receiving post-acute care discharge services from a large home health agency in New York City in 2019 (n = 45,390 enrollment episodes). METHODS: We developed a natural language processing algorithm for identifying information documented in free-text clinical notes (n = 1,429,030 notes) related to 4 categories: evidence of close relationships, evidence of advance directives, evidence suggesting lack of close relationships, and evidence suggesting lack of advance directives. We validated the algorithm against Gold Standard clinician review for 50 patients (n = 314 notes) to calculate precision, recall, and F-score. RESULTS: Algorithm performance for identifying text related to the 4 categories was excellent (average F-score = 0.91), with the best results for "evidence of close relationships" (F-score = 0.99) and the worst results for "evidence of advance directives" (F-score = 0.86). The algorithm identified 22% of all clinical notes (313,290 of 1,429,030) as having text related to 1 or more categories. More than 98% of enrollment episodes (48,164 of 49,141) included at least 1 clinical note containing text related to 1 or more categories. CONCLUSIONS AND IMPLICATIONS: This study establishes the feasibility of creating an automated screening algorithm to aid home health care agencies with identifying patients at risk of becoming INEADS. This screening algorithm can be applied as part of a multipronged approach to facilitate clinician support for advance care planning with patients at risk of becoming INEADS.

Using Natural Language Processing to Identify Home Health Care Patients at Risk for Diagnosis of Alzheimer's Disease and Related Dementias.

This study aimed to: (1) validate a natural language processing (NLP) system developed for the home health care setting to identify signs and symptoms of Alzheimer's disease and related dementias (ADRD) documented in clinicians' free-text notes; (2) determine whether signs and symptoms detected via NLP help to identify patients at risk of a new ADRD diagnosis within four years after admission. This study applied NLP to a longitudinal dataset including medical record and Medicare claims data for 56,652 home health care patients and Cox proportional hazard models to the subset of 24,874 patients admitted without an ADRD diagnosis. Selected ADRD signs and symptoms were associated with increased risk of a new ADRD diagnosis during follow-up, including: motor issues; hoarding/cluttering; uncooperative behavior; delusions or hallucinations; mention of ADRD disease names; and caregiver stress. NLP can help to identify patients in need of ADRD-related evaluation and support services.

A rapid review on current and potential uses of large language models in nursing.

BACKGROUND: The application of large language models across commercial and consumer contexts has grown exponentially in recent years. However, a gap exists in the literature on how large language models can support nursing practice, education, and research. This study aimed to synthesize the existing literature on current and potential uses of large language models across the nursing profession. METHODS: A rapid review of the literature, guided by Cochrane rapid review methodology and PRISMA reporting standards, was conducted. An expert health librarian assisted in developing broad inclusion criteria to account for the emerging nature of literature related to large language models. Three electronic databases (i.e., PubMed, CINAHL, and Embase) were searched to identify relevant literature in August 2023. Articles that discussed the development, use, and application of large language models within nursing were included for analysis. RESULTS: The literature search identified a total of 2028 articles that met the inclusion criteria. After systematically reviewing abstracts, titles, and full texts, 30 articles were included in the final analysis. Nearly all (93 %; n = 28) of the included articles used ChatGPT as an example, and subsequently discussed the use and value of large language models in nursing education (47 %; n = 14), clinical practice (40 %; n = 12), and research (10 %; n = 3). While the most common assessment of large language models was conducted by human evaluation (26.7 %; n = 8), this analysis also identified common limitations of large language models in nursing, including lack of systematic evaluation, as well as other ethical and legal considerations. DISCUSSION: This is the first review to summarize contemporary literature on current and potential uses of large language models in nursing practice, education, and research. Although there are significant opportunities to apply large language models, the use and adoption of these models within nursing have elicited a series of challenges, such as ethical issues related to bias, misuse, and plagiarism. CONCLUSION: Given the relative novelty of large language models, ongoing efforts to develop and implement meaningful assessments, evaluations, standards, and guidelines for applying large language models in nursing are recommended to ensure appropriate, accurate, and safe use. Future research along with clinical and educational partnerships is needed to enhance understanding and application of large language models in nursing and healthcare.

The ChatGPT Effect: Nursing Education and Generative Artificial Intelligence.

This article examines the potential of generative artificial intelligence (AI), such as ChatGPT (Chat Generative Pre-trained Transformer), in nursing education and the associated challenges and recommendations for their use. Generative AI offers potential benefits such as aiding students with assignments, providing realistic patient scenarios for practice, and enabling personalized, interactive learning experiences. However, integrating generative AI in nursing education also presents challenges, including academic integrity issues, the potential for plagiarism and copyright infringements, ethical implications, and the risk of producing misinformation. Clear institutional guidelines, comprehensive student education on generative AI, and tools to detect AI-generated content are recommended to navigate these challenges. The article concludes by urging nurse educators to harness generative AI's potential responsibly, highlighting the rewards of enhanced learning and increased efficiency. The careful navigation of these challenges and strategic implementation of AI is key to realizing the promise of AI in nursing education. [J Nurs Educ. 2024;63(X):XXX-XXX.].

Home Care Clinicians' Perspectives on Advance Care Planning for Patients at Risk for Becoming Incapacitated With No Evident Advance Directives or Surrogates.

Advance care planning is important and timely for patients receiving home health services; however, opportunities to facilitate awareness and engagement in this setting are often missed. This qualitative descriptive study elicited perspectives of home health nurses and social workers regarding barriers and facilitators to creating advance care plans in home health settings, with particular attention to patients with few familial or social contacts who can serve as surrogate decision-makers. We interviewed 15 clinicians employed in a large New York City-based home care agency in 2021-2022. Participants reported a multitude of barriers to supporting patients with advance care planning at the provider level (eg, lack of time and professional education, deferment, discomfort), patient level (lack of knowledge, mistrust, inadequate support, deferment, language barriers), and system level (eg, discontinuity of care, variations in advance care planning documents, legal concerns, lack of institutional protocols and centralized information). Participants noted that greater socialization and connection to existing educational resources regarding the intended purpose, scope, and applicability of advance directives could benefit home care patients.

Representation of Environmental Concepts Associated with Health Impacts in Computer Standardized Clinical Terminologies.

OBJECTIVE: To evaluate the representation of environmental concepts associated with health impacts in standardized clinical terminologies. METHODS: This study used a descriptive approach with methods informed by a procedural framework for standardized clinical terminology mapping. The United Nations Global Indicator Framework for the Sustainable Development Goals and Targets was used as the source document for concept extraction. The target terminologies were the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and the International Classification for Nursing Practice (ICNP). Manual and automated mapping methods were utilized. The lists of candidate matches were reviewed and iterated until a final mapping match list was achieved. RESULTS: A total of 119 concepts with 133 mapping matches were added to the final SNOMED CT list. Fifty-three (39.8%) were direct matches, 37 (27.8%) were narrower than matches, 35 (26.3%) were broader than matches, and 8 (6%) had no matches. A total of 26 concepts with 27 matches were added to the final ICNP list. Eight (29.6%) were direct matches, 4 (14.8%) were narrower than, 7 (25.9%) were broader than, and 8 (29.6%) were no matches. CONCLUSION: Following this evaluation, both strengths and gaps were identified. Gaps in terminology representation included concepts related to cost expenditures, affordability, community engagement, water, air and sanitation. The inclusion of these concepts is necessary to advance the clinical reporting of these environmental and sustainability indicators. As environmental concepts encoded in standardized terminologies expand, additional insights into data and health conditions, research, education, and policy-level decision-making will be identified.

Social Risk Factors are Associated with Risk for Hospitalization in Home Health Care: A Natural Language Processing Study.

OBJECTIVE: This study aimed to develop a natural language processing (NLP) system that identified social risk factors in home health care (HHC) clinical notes and to examine the association between social risk factors and hospitalization or an emergency department (ED) visit. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: We used standardized assessments and clinical notes from one HHC agency located in the northeastern United States. This included 86,866 episodes of care for 65,593 unique patients. Patients received HHC services between 2015 and 2017. METHODS: Guided by HHC experts, we created a vocabulary of social risk factors that influence hospitalization or ED visit risk in the HHC setting. We then developed an NLP system to automatically identify social risk factors documented in clinical notes. We used an adjusted logistic regression model to examine the association between the NLP-based social risk factors and hospitalization or an ED visit. RESULTS: On the basis of expert consensus, the following social risk factors emerged: Social Environment, Physical Environment, Education and Literacy, Food Insecurity, Access to Care, and Housing and Economic Circumstances. Our NLP system performed "very good" with an F score of 0.91. Approximately 4% of clinical notes (33% episodes of care) documented a social risk factor. The most frequently documented social risk factors were Physical Environment and Social Environment. Except for Housing and Economic Circumstances, all NLP-based social risk factors were associated with higher odds of hospitalization and ED visits. CONCLUSIONS AND IMPLICATIONS: HHC clinicians assess and document social risk factors associated with hospitalizations and ED visits in their clinical notes. Future studies can explore the social risk factors documented in HHC to improve communication across the health care system and to predict patients at risk for being hospitalized or visiting the ED.

HomeADScreen: Developing Alzheimer's disease and related dementia risk identification model in home healthcare.

BACKGROUND: More than 50 % of patients with Alzheimer's disease and related dementia (ADRD) remain undiagnosed. This is specifically the case for home healthcare (HHC) patients. OBJECTIVES: This study aimed at developing HomeADScreen, an ADRD risk screening model built on the combination of HHC patients' structured data and information extracted from HHC clinical notes. METHODS: The study's sample included 15,973 HHC patients with no diagnosis of ADRD and 8,901 patients diagnosed with ADRD across four follow-up time windows. First, we applied two natural language processing methods, Word2Vec and topic modeling methods, to extract ADRD risk factors from clinical notes. Next, we built the risk identification model on the combination of the Outcome and Assessment Information Set (OASIS-structured data collected in the HHC setting) and clinical notes-risk factors across the four-time windows. RESULTS: The top-performing machine learning algorithm attained an Area under the Curve = 0.76 for a four-year risk prediction time window. After optimizing the cut-off value for screening patients with ADRD (cut-off-value = 0.31), we achieved sensitivity = 0.75 and an F1-score = 0.63. For the first-year time window, adding clinical note-derived risk factors to OASIS data improved the overall performance of the risk identification model by 60 %. We observed a similar trend of increasing the model's overall performance across other time windows. Variables associated with increased risk of ADRD were "hearing impairment" and "impaired patient ability in the use of telephone." On the other hand, being "non-Hispanic White" and the "absence of impairment with prior daily functioning" were associated with a lower risk of ADRD. CONCLUSION: HomeADScreen has a strong potential to be translated into clinical practice and assist HHC clinicians in assessing patients' cognitive function and referring them for further neurological assessment.

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.

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.

Identifying Type II workplace violence from clinical notes using natural language processing.

BACKGROUND: Type II workplace violence in health care, perpetrated by patients/clients toward home healthcare nurses, is a serious health and safety issue. A significant portion of violent incidents are not officially reported. Natural language processing can detect these "hidden cases" from clinical notes. In this study, we computed the 12-month prevalence of Type II workplace violence from home healthcare nurses' clinical notes by developing and utilizing a natural language processing system. METHODS: Nearly 600,000 clinical visit notes from two large U.S.-based home healthcare agencies were analyzed. The notes were recorded from January 1, 2019 to December 31, 2019. Rule- and machine-learning-based natural language processing algorithms were applied to identify clinical notes containing workplace violence descriptions. RESULTS: The natural language processing algorithms identified 236 clinical notes that included Type II workplace violence toward home healthcare nurses. The prevalence of physical violence was 0.067 incidents per 10,000 home visits. The prevalence of nonphysical violence was 3.76 incidents per 10,000 home visits. The prevalence of any violence was four incidents per 10,000 home visits. In comparison, no Type II workplace violence incidents were recorded in the official incident report systems of the two agencies in this same time period. CONCLUSIONS AND APPLICATION TO PRACTICE: Natural language processing can be an effective tool to augment formal reporting by capturing violence incidents from daily, ongoing, large volumes of clinical notes. It can enable managers and clinicians to stay informed of potential violence risks and keep their practice environment safe.