National Healthcare Quality and Disparities Report
Latest available findings on quality of and access to health care
Data
- Data Infographics
- Data Visualizations
- Data Tools
- Data Innovations
- All-Payer Claims Database
- Healthcare Cost and Utilization Project (HCUP)
- Medical Expenditure Panel Survey (MEPS)
- AHRQ Quality Indicator Tools for Data Analytics
- State Snapshots
- United States Health Information Knowledgebase (USHIK)
- Data Sources Available from AHRQ
Search All Research Studies
AHRQ Research Studies
Sign up: AHRQ Research Studies Email updates
Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 10 of 10 Research Studies DisplayedHobensack M, Song J, Chae S
Capturing concerns about patient deterioration in narrative documentation in home healthcare.
This study aimed to build machine learning algorithms to identify “concerning” narrative notes of home healthcare (HHC) patients and identify emergency themes to support early identification of patients at risk for deterioration. Six algorithms were applied to 4000 narrative notes from a HHC agency to classify notes as either "concerning" or "not concerning." Emerging themes were identified using Latent Dirichlet Allocation bag of words topic modeling. Emerging themes of concern included patient-clinician communication, HHC services provided, gait challenges, mobility concerns, wounds, and caregivers. Most of these themes had already been identified in previous literature as increasing risk for adverse events.
AHRQ-funded; HS027742.
Citation: Hobensack M, Song J, Chae S .
Capturing concerns about patient deterioration in narrative documentation in home healthcare.
AMIA Annu Symp Proc 2023 Apr 29; 2022:552-59..
Keywords: Home Healthcare, Electronic Health Records (EHRs), Health Information Technology (HIT)
Hobensack M, Song J, Scharp D
Machine learning applied to electronic health record data in home healthcare: a scoping review.
This literature review aimed to synthesize and appraise the literature describing the application of machine learning to predict adverse outcomes (e.g., hospitalization, mortality) using electronic health record (EHR) data in the home healthcare (HHC) setting. The secondary aim was to evaluate the comprehensiveness of predictors used in the machine learning algorithms guided by the Biopsychosocial Model. Studies were included if they 1) described services provided in the HHC setting, 2) applied machine learning algorithms to predict adverse outcomes, defined as outcomes related to patient deterioration, 3) used EHR data and, 4) focused on the adult population. Predictors were mapped to the Biopsychosocial Model. The final sample included 20 studies, of which 18 used predictors from standardized assessments integrated in the EHR. The most common outcome was hospitalization (55%), followed by mortality (25%). About 35% of studies excluded psychological predictors. Most studies (75%) demonstrated high or unclear risk of bias with tree based algorithms most frequently applied (75%).
AHRQ-funded; HS027742.
Citation: Hobensack M, Song J, Scharp D .
Machine learning applied to electronic health record data in home healthcare: a scoping review.
Int J Med Inform 2023 Feb; 170:104978. doi: 10.1016/j.ijmedinf.2022.104978..
Keywords: Home Healthcare, Electronic Health Records (EHRs), Health Information Technology (HIT)
Hobensack M, Ojo M, Barrón Y
Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians.
The objectives of this study were to identify risk factors that home healthcare clinicians associate with patient deterioration and to understand clinicians’ response to and documentation of these risk factors. The authors interviewed multidisciplinary home healthcare clinicians and used directed content analysis to identify risk factors for deterioration. A total of 79 risk factors were identified by the clinicians, who responded most often by communicating with the prescribing provider or following up with patients and caregivers. Clinicians also acknowledged that social factors played a role in deterioration risk. The authors noted that, since most risk factors were documented in clinical notes, methods such as natural language processing are needed to extract them. They concluded that by providing a comprehensive list of risk factors grounded in clinician expertise and mapped to standardized terminologies, the results of their study supported the development of an early warning system for patient deterioration.
AHRQ-funded; HS027742.
Citation: Hobensack M, Ojo M, Barrón Y .
Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians.
J Am Med Inform Assoc 2022 Apr 13;29(5):805-12. doi: 10.1093/jamia/ocac023..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Home Healthcare, Risk, Hospitalization
Sockolow PS, Bowles KH, Le NB
There's a problem with the problem list: incongruence of patient problem information across the home care admission.
The purpose of this observational field study was to illustrate patterns of patient problem information received and documented across the home health care (HHC) admission process and offer practice, policy, and health information technology recommendations to improve information transfer. The investigators concluded that diagnosis or problem information transferred from the referral source or gathered during an in-home assessment did not appear in the plan of care. Because of the EHR structure, clinicians could not identify inactive problem or problem priority.
AHRQ-funded; HS024537.
Citation: Sockolow PS, Bowles KH, Le NB .
There's a problem with the problem list: incongruence of patient problem information across the home care admission.
J Am Med Dir Assoc 2021 May;22(5):1009-14. doi: 10.1016/j.jamda.2020.06.032..
Keywords: Home Healthcare, Electronic Health Records (EHRs), Health Information Technology (HIT)
Topaz M, Woo K, Ryvicker M
Home healthcare clinical notes predict patient hospitalization and emergency department visits.
About 30% of home healthcare patients are hospitalized or visit an emergency department (ED) during a home healthcare (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes. The aim of the study was to identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes).
AHRQ-funded; HS027742.
Citation: Topaz M, Woo K, Ryvicker M .
Home healthcare clinical notes predict patient hospitalization and emergency department visits.
Nurs Res 2020 Nov/Dec;69(6):448-54. doi: 10.1097/nnr.0000000000000470..
Keywords: Elderly, Home Healthcare, Emergency Department, Hospitalization, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)
Sockolow PS, Bowles KH, Wojciechowicz C
Incorporating home healthcare nurses' admission information needs to inform data standards.
Patient transitions into home health care (HHC) often occur without the transfer of information needed for critical clinical decisions and the plan of care. Owing to a lack of universally implemented standards, there is wide variation in information transfer. In this study, the investigators sought to characterize missing information at HHC admission. They conducted a mixed methods study with 3 diverse HHC agencies.
AHRQ-funded; HS024537.
Citation: Sockolow PS, Bowles KH, Wojciechowicz C .
Incorporating home healthcare nurses' admission information needs to inform data standards.
J Am Med Inform Assoc 2020 Aug;27(8):1278-86. doi: 10.1093/jamia/ocaa087..
Keywords: Home Healthcare, Transitions of Care, Electronic Health Records (EHRs), Health Information Technology (HIT)
Sockolow PS, Bass EJ, Ynag Y
Availability and quality of information used by nurses while admitting patients to a rural home health care agency.
This study investigated the availability and quality of information used by nurses in a rural home health care agency during patient admission. Twelve in-home admissions were observed and the nurses were interviewed before and after the admissions process. Content and quality of documents available to the nurses was analyzed and needed documents were not uniformly present. They rarely received visit pattern or medication management information. These results show the need for a high quality electronic health record system.
AHRQ-funded; HS024537.
Citation: Sockolow PS, Bass EJ, Ynag Y .
Availability and quality of information used by nurses while admitting patients to a rural home health care agency.
Stud Health Technol Inform 2019 Aug 21;264:798-802. doi: 10.3233/shti190333..
Keywords: Rural Health, Home Healthcare, Electronic Health Records (EHRs), Health Information Technology (HIT), Provider: Nurse, Provider
Yang Y, Bass EJ, Bowles KH
Impact of home care admission nurses' goals on electronic health record documentation strategies at the point of care.
The article reports on a study designed to investigate documentation strategies used by home care nurses with respect to entering electronic data during admission as well as the effect of nursing goals on the process. This was done to characterize admission nurses' practices at the point of care and to establish a basis for design recommendations for electronic health records (EHRs). Five nurses in rural Pennsylvania home care agencies were observed during the admission process. The results of the study lead the authors to recommend that EHR design and training should support the manner in which home care nurses document patient encounters.
AHRQ-funded; HS024537.
Citation: Yang Y, Bass EJ, Bowles KH .
Impact of home care admission nurses' goals on electronic health record documentation strategies at the point of care.
Comput Inform Nurs 2019 Jan;37(1):39-46. doi: 10.1097/cin.0000000000000468..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Home Healthcare, Nursing
Sockolow PS, Bowles KH, Adelsberger MC
Challenges and facilitators to adoption of a point-of-care electronic health record in home care.
In order to identify challenges to the adoption of electronic health records (EHR) in the home care setting, the researchers assessed clinician satisfaction, informed by workflow and patient outcomes. Using a combination of surveys, observations, and interviews in an agency with 137 clinicians, the researchers found that adoption challenges included: (a) hardware problems coupled with lack of field support; (b) inadequate training; and (c) mismatch of EHR usability/functionality and workflow.
AHRQ-funded; HS021008.
Citation: Sockolow PS, Bowles KH, Adelsberger MC .
Challenges and facilitators to adoption of a point-of-care electronic health record in home care.
Home Health Care Serv Q 2014;33(1):14-35. doi: 10.1080/01621424.2013.870098..
Keywords: Health Information Technology (HIT), Electronic Health Records (EHRs), Home Healthcare
Sockolow PS, Bowles KH, Adelsberger MC
Impact of homecare electronic health record on timeliness of clinical documentation, reimbursement, and patient outcomes.
The study objective was to assess the impact of attaining efficiency and health goals of a point-of-care EHR in home care. To realize this objective, the researchers compared clinical documentation workflow, financial time-to-billing and patient physiological and behavioral outcomes to evaluate its impact. The findings indicate that EHR use significantly improved the timeliness of clinical documentation and billing for reimbursement but had limited impact on improving patient outcomes.
AHRQ-funded; HS021008.
Citation: Sockolow PS, Bowles KH, Adelsberger MC .
Impact of homecare electronic health record on timeliness of clinical documentation, reimbursement, and patient outcomes.
Appl Clin Inform 2014;5(2):445-62. doi: 10.4338/aci-2013-12-ra-0106..
Keywords: Health Information Technology (HIT), Electronic Health Records (EHRs), Home Healthcare