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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., (Copyright © 2023 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)

Objectives: This study explored the association between the timing of the first home health care nursing visits (start-of-care visit) and 30-day rehospitalization or emergency department (ED) visits among patients discharged from hospitals., Design: Our cross-sectional study used data from 1 large, urban home health care agency in the northeastern United States., Setting/participants: We analyzed data for 49,141 home health care episodes pertaining to 45,390 unique patients who were admitted to the agency following hospital discharge during 2019., Methods: We conducted multivariate logistic regression analyses to examine the association between start-of-care delays and 30-day hospitalizations and ED visits, adjusting for patients' age, race/ethnicity, gender, insurance type, and clinical and functional status. We defined delays in start-of-care as a first nursing home health care visit that occurred more than 2 full days after the hospital discharge date., Results: During the study period, we identified 16,251 start-of-care delays (34% of home health care episodes), with 14% of episodes resulting in 30-day rehospitalization and ED visits. Delayed episodes had 12% higher odds of rehospitalization or ED visit (OR 1.12; 95% CI: 1.06-1.18) compared with episodes with timely care., Conclusions and Implications: The findings suggest that timely start-of-care home health care nursing visit is associated with reduced rehospitalization and ED use among patients discharged from hospitals. With more than 6 million patients who receive home health care services across the United States, there are significant opportunities to improve timely care delivery to patients and improve clinical outcomes., (Copyright © 2022 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objectives: Home health care patients have critical needs requiring timely care following hospital discharge. Although Medicare requires timely start-of-care nursing visits, a significant portion of home health care patients wait longer than 2 days for the first visit. No previous studies investigated the pattern of start-of-care visits or factors associated with their timing. This study's purpose was to examine variation in timing of start-of-care visits and characterize patients with visits later than 2 days postdischarge., Design: Retrospective cohort study., Setting/participants: Patients admitted to a large, Northeastern US, urban home health care organization during 2019. The study included 48,497 home care episodes for 45,390 individual patients., Measurement: We calculated time to start of care from hospital discharge for 2 patient groups: those seen within 2 days vs those seen >2 days postdischarge. We examined patient factors, hospital discharge factors, and timing of start of care using multivariate logistic regression., Results: Of 48,497 episodes, 16,251 (33.5%) had a start-of-care nursing visit >2 days after discharge. Increased odds of this time frame were associated with being black or Hispanic and having solely Medicaid insurance. Odds were highest for patients discharged on Fridays, Saturdays, and Mondays. Factors associated with visits within 2 days included surgical wound presence, urinary catheter, pain, 5 or more medications, and intravenous or infusion therapies at home., Conclusions and Implications: Findings provide the first publication of clinical and demographic characteristics associated with home health care start-of-care timing and its variation. Further examination is needed, and adjustments to staff scheduling and improved information transfer are 2 suggested interventions to decrease variation., (Copyright © 2021 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)

Objective: To profile the characteristics of growing numbers of sepsis survivors receiving home healthcare (HHC) by type of sepsis before, during, and after a sepsis hospitalization and identify characteristics significantly associated with 7-day readmission., Design: Cross-sectional descriptive study. Data sources included the Outcome and Assessment Information Set (OASIS) and Medicare administrative and claims data., Setting and Participants: National sample of Medicare beneficiaries hospitalized for sepsis who were discharged to HHC between July 1, 2013 and June 30, 2014 (N = 165,228)., Methods: We used an indicator distinguishing among 3 types of sepsis: explicitly coded sepsis diagnosis without organ dysfunction; severe sepsis with organ dysfunction; and septic shock. We compared these subgroups' demographic, clinical and functional characteristics, comorbidities, risk factors for rehospitalization, characteristics of the index hospital stay, and predicted 7-day hospital readmission., Results: The majority (80.7%) had severe sepsis, 5.7% had septic shock, and 13.6% had sepsis without acute organ system dysfunction. The medical diagnoses recorded at HHC admission identified sepsis or blood infection only 7% of the time, potentially creating difficulty identifying the sepsis survivor in HHC. Among sepsis types, septic shock survivors had the greatest illness burden profile. This study describes 12 key variables, each of which individually raises the relative 7-day readmission risk by as much as 60%. Increased risk of 7-day rehospitalization was found among those with septic shock, 3 or more previous inpatient stays, index hospital length of stay of >8 days, dyspnea, >6 functional dependencies, and other risk factors., Conclusions and Implications: Implications for practice include using our findings to identify sepsis survivors who are at risk for early readmission. Assessment for these factors may profile the at-risk patient, thereby triggering the call for additional acute care intervention such as delayed discharge, or post-acute intervention such as early home visit and outpatient follow-up., (Copyright © 2019 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)

Objectives: This study sought to: 1) identify the predictors of hospice enrollment for patients with heart failure (HF); and 2) determine the impact of hospice enrollment on health care use., Background: Patients with HF rarely enroll in hospice. Little is known about how hospice affects this group's health care use., Methods: Using a propensity score-matched sample of Medicare decedents with ≥2 HF discharges within 6 months, an Outcome and Assessment Information Set (OASIS) assessment, and subsequent death, we used Medicare administrative, claims, and patient assessment data to compare hospitalizations, intensive care unit stays, and emergency department visits for those beneficiaries who enrolled in hospice and those who did not., Results: The propensity score-matched sample included 3,067 beneficiaries in each group with a mean age of 82 years; 53% were female, and 15% were Black, Asian, or Hispanic. For objective 1, there were no differences in the characteristics, symptom burden, or functional status between groups that were associated with hospice enrollment. For objective 2, in the 6 months after the second HF discharge, the hospice group had significantly fewer emergency department visits (2.64 vs. 2.82; p = 0.04), hospital days (3.90 vs. 4.67; p < 0.001), and intensive care unit stays (1.25 vs. 1.51; p < 0.001); they were less likely to die in the hospital (3% vs. 56%; p < 0.001), and they had longer median survival (80 days vs. 71 days; log-rank test p = 0.004)., Conclusions: Beneficiaries' characteristics, including symptom burden and functional status, do not predict hospice enrollment. Those patients who enrolled in hospice used less health care, survived longer, and were less likely to die in the hospital. A tailored hospice model may be needed to increase enrollment and offer benefits to patients with HF., (Published by Elsevier Inc.)

Background: US federal policies are incentivizing use of electronic prescribing (e-prescribing) to improve safety. However, little is known about e-prescribing's actual impact on medication safety over time. A study was conducted to assess the effect of implementing a commercial electronic health record (EHR) with e-prescribing on rates and types of prescribing errors. Understanding safety effects from e-prescribing will be important as providers increasingly e-prescribe., Methods: Prescriptions written by 20 community-based primary care providers in the Hudson Valley region of New York from November 2008 to November 2009 were retrospectively studied. All providers adopted a commercial EHR with robust clinical decision support and extensive technical support to aid in prescribing. Errors were identified by standardized prescription and chart review., Results: Some 1,629 prescriptions were analyzed at three months postimplementation, and 1,738 prescriptions were analyzed at one year postimplementation. Use of e-prescribing resulted in relatively low error rates (6.0 errors per 100 prescriptions). These rates were sustained over time but without further improvement (6.0 versus 4.5 errors per 100 prescriptions, p = .15). Antibiotics were the class of medications most frequently involved (12.7% of overall errors), and direction errors were most common (24% of errors)., Conclusions: This study is the first, as far as known, to quantitatively evaluate prescribing errors early after EHR implementation and after sustained use among community-based primary care providers. Relatively low rates of errors with e-prescribing were found early and after prolonged use. Extensive support for providers before, during, and after implementation may mitigate potential safety threats from implementation of an EHR system and result in sustained safety benefits over the long-term.

Background: Health policy forces are promoting the adoption of interoperable electronic health records (EHRs) with electronic prescribing (e-prescribing). Despite the promise of EHRs with e-prescribing to improve medication safety in ambulatory care settings--where most prescribing occurs and where errors are common--few studies have demonstrated its effectiveness. A study was conducted to assess the effect of an e-prescribing system with clinical decision support, including checks for drug allergies and drug-drug interactions, that was integrated within an EHR on rates of ambulatory prescribing errors., Methods: In a prospective study using a nonrandomized, pre-post design with concurrent controls, 6 providers who used a commercial e-prescribing system were compared with 15 providers who remained paper-based from September 2005 through July 2008. Prescribing errors were identified by a standardized prescription and chart review., Results: Some 2,432 paper prescriptions at baseline and 2,079 prescriptions at one year were analyzed. Error rates for e-prescribing adopters decreased 1.5-fold--from 26.0 errors per 100 prescriptions at baseline (95% confidence interval [CI], 17.4-38.9) to 16.0 errors per 100 prescriptions at one year (95% CI, 12.7-20.2; p = .09). Error rates remained unchanged for nonadopters (37.3 per 100 prescriptions at baseline, 95% CI, 27.6-50.2, versus 38.4 per 100 prescriptions at one year, 95% CI 27.4-53.9; p = .54). Error rates for e-prescribing adopters were significantly lower than for nonadopters at one year (p < .001). Illegibility errors were high at baseline and eliminated by e-prescribing., Conclusions: The preliminary findings from this small group of providers suggest that e-prescribing systems may decrease ambulatory prescribing errors, which are occurring at high rates among community-based providers.

Background: Electronic health records (EHRs), in combination with health information exchange, are being promoted in the United States as a strategy for improving quality of care. No single metric set exists for measuring the effectiveness of these interventions. A set of quality metrics was sought that could be retrieved electronically and would be sensitive to the changes in quality that EHRs with health information exchange may contribute to ambulatory care., Methods: A literature search identified quality metric sets for ambulatory care. Two rounds of quantitative rating of individual metrics were conducted. Metrics were developed de novo to capture additional expected effects of EHRs with health information exchange. A 36-member national expert panel validated the rating process and final metric set., Results: Seventeen metric sets containing 1,064 individual metrics were identified; 510 metrics met inclusion criteria. Two rounds of rating narrowed these to 59 metrics and then to 18. The final 18 consisted of metrics for asthma, cardiovascular disease, congestive heart failure, diabetes, medication and allergy documentation, mental health, osteoporosis, and prevention. Fourteen metrics were developed de novo to address test ordering, medication management, referrals, follow-up after discharge, and revisits., Discussion: The novel set of 32 metrics is proposed as suitable for electronic reporting to capture the potential quality effects of EHRs with health information exchange. This metric set may have broad utility as health information technology becomes increasingly common with funding from the federal stimulus package and other sources. This work may also stimulate discussion on improving how data are entered and extracted from clinically rich, electronic sources, with the goal of more accurately measuring and improving care.

Objective: Refinement of endoscopic pituitary surgery requires an understanding of the impact of demographic and surgical variables on outcomes., Methods: Multivariate logistic regression and ANOVA models were used to explore variables for association with outcomes in a consecutive series of 57 patients undergoing endoscopic pituitary surgery., Results: The mean duration of surgery was 177 min and was longer in patients with larger tumor size (p=0.03) and presentation with visual symptoms (p=0.02) in univariate analyses. The median duration of hospitalization was 3 days and was longer in patients with larger tumors (p=0.0005). Gross tumor removal was achieved in 89%. Tumor size correlated with extent of tumor removal with an almost 3-fold decrease in complete tumor removal for every 1cm increase in tumor size (p=0.047). High rates of hormonal control (90%) and improvement in visual symptoms (92%) were noted., Conclusions: High rates of gross tumor removal, hormonal cure and visual field improvement were noted in this series. Markers including tumor size and visual symptoms may be used to stratify patients.

Objectives: Our purpose was to conduct a longitudinal investigation of xerostomia and salivary gland hypofunction in a national cohort of HIV-positive and at-risk HIV-negative participants in the Women's Interagency HIV Study. Study design. Data included responses to a dry mouth questionnaire, clinical evaluations of major salivary glands, and unstimulated and chewing-stimulated whole salivary flow rates. Repeated measures regression models were used to determine factors associated with xerostomia and salivary gland hypofunction., Results: Significant univariate associations were found between HIV status and reports of "too little saliva" (P <.0001), < or = 0.1 mL/min, unstimulated saliva (P =.01), and lack of saliva upon palpation of parotid (P =.02) and submandibular/sublingual salivary glands (P =.03). Adjusted odds of reports of "too little saliva" were significantly higher for HIV-positive participants (odds ratio [OR] = 2.44; 95% CI, 1.49 - 3.97; P =.0004) than for HIV-negative participants. Among HIV-positive women, adjusted odds of reports of "too little saliva" and of < or = 0.7 mL/min chewing-stimulated saliva were significantly higher for those with CD4 < 200 (OR = 1.58; 95% CI, 1.07-2.34; P =.022; and OR = 1.53; 95% CI, 1.05-2.23; P =.027, respectively) and for those with CD4 200-500 (OR = 1.47; 95%CI, 1.07-2.02; P = 0.016; and OR = 1.37; 95% CI, 1.01-2.31; P =.001, respectively) than for those with CD4 > 500. Also, adjusted odds of < or = 0.1mL/min unstimulated saliva and < or = 0.7 mL/min chewing-stimulated saliva were significantly higher in women on highly active antiretroviral therapy (HAART) (OR = 1.25; 95% CI, 1.05 - 1.50; P =.014) than in women not on HAART (OR = 1.34; 95% CI, 1.01 - 1.79; P =.044)., Conclusions: HIV-positive women are at a significantly higher risk for xerostomia and salivary gland hypofunction than HIV-negative women, and low CD4 cell counts and HAART use are significant risk factors for these conditions.