Table 2.
Characteristics of Sources of Evidence
| Authors, year | Country | Study Purpose | Study Setting | Study Population; Characteristics (Age, Gender, Race, and Ethnicity) if Specified | Study Design | Data Source and Type | Total Number of Clinical Notes Analyzed | Total Number of Distinct Patients | Note Type and Author |
|---|---|---|---|---|---|---|---|---|---|
| Bjarnadottir et al.33 | United States | To examine documentation of sexual orientation and gender identity in HHC nurses’ narrative notes. | HHC | Patients who received HHC services in Manhattan in 2015 | Retrospective observational | EHR, unstructured data | 862,715 | 20,447 | Narrative, referral, and coordination of care notes; nurses |
| Chae et al.34 | United States | To identify patients with heart failure in HHC with poor self-management using NLP. | HHC | Patients with heart failure who received HHC services from one of the largest HHC agencies in the Northeastern United States between 2015 and 2017 | Retrospective observational | EHR, unstructured data | ~2.3 million | Not specified | Visit and care coordination notes; clinicians |
| Chae et al.35 | United States | To identify HHC patients with heart failure who have poor self-management using NLP. | HHC | Patients admitted to HHC after being discharged from the hospital with a diagnosis of heart failure between 2015 and 2017; older adults (mean age 81.56 years), 60.9% male, 13.52% non-Hispanic Black, 37.30% non-Hispanic White, 11.56% Hispanic, 37.62% Asian/Other/Unknown/Native Hawaiian or Pacific Islander | Retrospective observational | EHR, structured and unstructured data | 353,718 | 9710 | Visit and care coordination notes; nurses, social workers, physical and occupational therapists |
| Harkanen et al.36 | Finland | To identify terms that are related to the most common contributing factors to medication administration incidents in free-text narratives. | HHC | Patients with chronic illnesses, disabilities, or recovering from acute illnesses, receiving HHC services from the Helsinki Health Center | Retrospective cohort | Medication administration incident reports from the HaiPro incident reporting system; structured and unstructured data | 19,725 | Not specified | Incident reports; nurses, supervisors, students, social workers, physicians |
| Lohman et al.37 | United States | To evaluate the association between state policies about availability, regulation, and cost of LTC and suicide mortality over a 5-year time period. | LTC | Text narratives describing suicide deaths of older adults aged 55 years and older in LTC facilities reported between 2010 and 2015 | Longitudinal ecological | National Violent Death Reporting System (2010–2015); unstructured data | Not specified | 25,040 | Narratives providing detailed circumstances associated with a suicide that may not be accounted for in categorical data, such as the content of a suicide note or contributing circumstances preceding death; medical examiners |
| Mezuk et al.38 | United States | To estimate the number of suicides associated with residential LTC and identify whether machine learning tools can improve suicide surveillance data. | LTC | Adults aged 55 years and older in LTC facilities in 27 states who died by suicide or undetermined cause; 77.6% male, 90% non-Hispanic White | Cross-sectional | National Violent Death Reporting System (2003–2015); unstructured data | 47,759 | 47,759 | Narrative reports; coroners, medical examiners |
| Pesko et al.39 | United States | To evaluate whether communication failures between HHC nurses and physicians during an episode of home care after hospital discharge are associated with hospital readmission, stratified by patients at high and low risk of readmission. | HHC | Patients with congestive heart failure who received HHC services from VNS Health from 2008 to 2009; mean age 80.46 years, 64% female, 54% White, 22% Black, 19% Hispanic, 5% Other/Unknown | Retrospective | EHR, CMS claims; structured and unstructured data | Not specified | Not specified | Free-text responses; nurses |
| Popejoy et al.40 | United States | To quantify care coordination by identifying care coordination activities used by Aging in Place nurse care coordinators and HHC nurses. | HHC | Two groups of patients: (1) patients admitted to a HHC agency between 1999 and 2002 for enhanced Aging in Place Care, (2) patients who received traditional HHC without enhanced care coordination between 2003 and 2005 | Cross-sectional | EHR; structured and unstructured data | 128,135 | 908 | Care coordination; nurses |
| Press et al.41 | United States | To identify failed communication attempts between HHC nurses and physicians using NLP to identify predictors of communication failure and assess the association with hospital readmission. | HHC | Medicare beneficiaries with congestive heart failure who received HHC services from VNS Health in 2008 and 2009; mean age 81 years, 62% female, 58% White, 19% African American, 18% Hispanic | Retrospective cohort | EHR, CMS claims; structured and unstructured data | 12,847 | 5698 | Free-text comments when attempting to contact physicians; nurses |
| Song et al.42 | United States | To extract factors from clinical notes to more accurately describe a patient’s condition regarding wounds to build predictive models to identify risk of wound infection—related hospitalization. | HHC | Patients who received HHC services from VNS Health in 2014; mean age 67.6, 56.7% female, 7% Asian or Pacific Islander, 23.5% Black, 20.6% Hispanic, 48.9% White | Retrospective secondary data analysis | EHR; structured and unstructured data | 2,610,757 | 112,789 | Visit and care coordination notes; not specified |
| Song et al.43 | United States | To compare the predictive performance of 4 risk models for hospitalization and emergency department visits in HHC. | HHC | Patients who received HHC services from VNS Health, largest not-for-profit HHC organization in the northeastern United States, mean age 78.8 years, 64% female, 56.7% non-Hispanic White, 21.8% non-Hispanic Black, 16% Hispanic, 5.5% Other | Retrospective cohort | EHR, structured and unstructured data | 2,321,977 | 66,317 | Visit notes and care coordination; nurses, social workers, physical therapists, occupational therapists |
| Song et al.44 | United States | To develop an NLP algorithm to identify concerning language for HHC patients’ risk of hospitalization or emergency department visit. | HHC | Patients admitted to HHC at VNS Health, a large urban HHC organization in the northeastern United States between 2015 and 2017 | Retrospective cohort | EHR, structured and unstructured data | 2,321,977 | 66,317 | Visit and care coordination notes; note author not specified |
| Song et al.45 | United States | To identify risk factor clusters in HHC and determine if clusters are associated with emergency department visits or hospitalizations. | HHC | Patients who received HHC services between 2015 and 2017; mean age 78.8 years, 64% female, 5.62% Asian, 16.8% Black, 12.9% Hispanic, 64.3% White | Retrospective observational cohort study | EHR, structured and unstructured data | 2,321,977 | 61,454 | Visit and care coordination notes; nurses, physical therapists, occupational therapists social workers |
| Topaz et al.46 | United States | To develop and evaluate an open-source software (called NimbleMiner) that allows clinicians to interact with word embedding models with a goal of creating lexicons of similar terms. | HHC | Patients treated by clinicians at one of the largest homecare agencies in the United States (located in New York, NY) during 2015 | Case study | EHR, unstructured data | 1,149,586 | 89,459 | Clinical notes ranged from lengthy admission notes (often written by an RN) to shorter progress notes (eg, physical therapy progress notes); nurses, physical therapists, occupational therapists, social work |
| Topaz et al.47 | United States | To describe the general system architecture and user interface and classify fall-related information (including fall history, fall prevention interventions, and fall risk) in homecare visit notes. | HHC | Patients treated by clinicians of the largest homecare agency in the United States (located in New York, NY) during 2015 | Cross-sectional | EHR, unstructured data | 1,149,586 | 89,459 | Visit notes; nurses, physical therapy, occupational therapy, social workers |
| Topaz et al.48 | United States | To develop an NLP algorithm to identify common neuropsychiatric symptoms of Alzheimer’s disease and related dementia in free-text clinical notes and describe symptom clusters and emergency department visits and hospitalization rates. | HHC | Patients in New York City with any diagnoses admitted for post-acute HHC services at the study organization during 2014; mean age 70.8 years, 60.8% female, 43% White, 27% Black or African American, 24% Hispanic/Latino, 6% Asian | Retrospective cohort | EHR, structured and unstructured data | 2,610,757 | 89,459 | Visit and care coordination notes; nurses, physical therapists, occupational therapists, social workers |
| Topaz et al.26 | United States | To identify patients at high risk for emergency department visits or hospitalizations using HHC clinical notes. | HHC | Patients who received HHC services from the largest nonprofit HHC agency in New York, NY in 2014; mean age 70.8 years, 60.8% female, 43% White, 27% Black or African American, 24% Hispanic/Latino, and 6% Asian | Retrospective cohort | EHR, structured and unstructured data | 727,676 | 89,459 | Visit notes; nurses, physical therapists, occupational therapists, social workers |
| Topaz et al.18 | United States | To identify documentation of 7 common symptoms (anxiety, cognitive disturbance, depressed mood, fatigue, sleep disturbance, pain, well-being) in HHC narrative nursing notes using NLP and examine the association between symptoms and ED visits or hospitalizations. | HHC | Patients admitted to the largest not-for-profit HHC provider in the United States, VNS Health, in 2014; mean age 70.8 years, 60.8% female, 43% White, 27% Black or African American, 24% Hispanic/Latino, and 6% Asian | Retrospective cohort | EHR; structured and unstructured data | 2,610,767 | 89,825 | Visit and care coordination; nurses |
| Woo et al.49 | United States | To develop an NLP algorithm to identify urinary tract infection —related information in nursing notes. | HHC | Patients treated by clinicians of the largest nonprofit home care agency in the United States (located in New York, NY) in 2014 | Cross-sectional | EHR; structured and unstructured data | 2,610,757 | 89,459 | Visit and care coordination; nurses |
| Woo et al.50 | United States | To develop an NLP algorithm to identify wound infection—related information from nursing notes, estimate wound infection prevalence in the HHC setting, and describe related patient characteristics by linking NLP-identified wound infections to structured data in HHC. | HHC | Patients who received HHC services from the largest nonprofit homecare agency in the United States in 2014; 57% female, 22% Black, 22% Hispanic, 52% White, 7% Asian | Retrospective cohort | EHR; structured and unstructured data | 2,610,757 | 89,459 | Visit notes and care coordination notes; nurses |
| Zolnoori et al.51 | United States | To develop and test an NLP algorithm that identifies reasons for delayed visits in HHC free-text clinical notes and describes reasons for delayed visits. | HHC | Patients admitted to the largest not-for-profit urban HHC agency in the United States during the calendar year of 2019 following a hospitalization; 61.8% older than 65 years, 57.9% female, 42.1% male, 43.5% non-Hispanic White, 23.9% non-Hispanic Black, 23.5% Hispanic, 9.2% Other | Retrospective cohort | EHR, structured and unstructured data | 118,767 | 45,390 | Nursing notes, intake clinical comment notes, insurance and other additional information notes, telephone communication notes, 17 other less frequent note categories; nurses, other HHC admission staff |
CMS, Centers for Medicare and Medicaid Services; ED, emergency department; HHC, home health care; LTC, long-term care; RN, registered nurse; VNS, visiting nurse service.