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JMIR Aging ; 5(3): e37482, 2022 Aug 23.
Article in English | MEDLINE | ID: covidwho-2022370

ABSTRACT

BACKGROUND: There are 15,632 nursing homes (NHs) in the United States. NHs continue to receive significant policy attention due to high costs and poor outcomes of care. One strategy for improving NH care is use of health information technology (HIT). A central concept of this study is HIT maturity, which is used to identify adoption trends in HIT capabilities, use and integration within resident care, clinical support, and administrative activities. This concept is guided by the Nolan stage theory, which postulates that a system such as HIT moves through a series of measurable stages. HIT maturity is an important component of the rapidly changing NH landscape, which is being affected by policies generated to protect residents, in part because of the pandemic. OBJECTIVE: The aim of this study is to identify structural disparities in NH HIT maturity and see if it is moderated by commonly used organizational characteristics. METHODS: NHs (n=6123, >20%) were randomly recruited from each state using Nursing Home Compare data. Investigators used a validated HIT maturity survey with 9 subscales including HIT capabilities, extent of HIT use, and degree of HIT integration in resident care, clinical support, and administrative activities. Each subscale had a possible HIT maturity score of 0-100. Total HIT maturity, with a possible score of 0-900, was calculated using the 9 subscales (3 x 3 matrix). Total HIT maturity scores equate 1 of 7 HIT maturity stages (stages 0-6) for each facility. Dependent variables included HIT maturity scores. We included 5 independent variables (ie, ownership, chain status, location, number of beds, and occupancy rates). Unadjusted and adjusted cumulative odds ratios were calculated using regression models. RESULTS: Our sample (n=719) had a larger proportion of smaller facilities and a smaller proportion of larger facilities than the national nursing home population. Integrated clinical support technology had the lowest HIT maturity score compared to resident care HIT capabilities. The majority (n=486, 60.7%) of NHs report stage 3 or lower with limited capabilities to communicate about care delivery outside their facility. Larger NHs in metropolitan areas had higher odds of HIT maturity. The number of certified beds and NH location were significantly associated with HIT maturity stage while ownership, chain status, and occupancy rate were not. CONCLUSIONS: NH structural disparities were recognized through differences in HIT maturity stage. Structural disparities in this sample appear most evident in HIT maturity, measuring integration of clinical support technologies for laboratory, pharmacy, and radiology services. Ongoing assessments of NH structural disparities is crucial given 1.35 million Americans receive care in these facilities annually. Leaders must be willing to promote equal opportunities across the spectrum of health care services to incentivize and enhance HIT adoption to balance structural disparities and improve resident outcomes.

2.
JMIR Form Res ; 6(1): e29647, 2022 Jan 27.
Article in English | MEDLINE | ID: covidwho-1662504

ABSTRACT

BACKGROUND: Patient portals allow communication with clinicians, access to test results, appointments, etc, and generally requires another set of log-ins and passwords, which can become cumbersome, as patients often have records at multiple institutions. Social credentials (eg, Google and Facebook) are increasingly used as a federated identity to allow access and reduce the password burden. Single Federated Identity Log-in for Electronic health records (Single-FILE) is a real-world test of the feasibility and acceptability of federated social credentials for patients to access their electronic health records (EHRs) at multiple organizations with a single sign-on (SSO). OBJECTIVE: This study aims to deploy a federated identity system for health care in a real-world environment so patients can safely use a social identity to access their EHR data at multiple organizations. This will help identify barriers and inform guidance for the deployment of such systems. METHODS: Single-FILE allowed patients to pick a social identity (such as Google or Facebook) as a federated identity for multisite EHR patient portal access with an SSO. Binding the identity to the patient's EHR records was performed by confirming that the patient had a valid portal log-in and sending a one-time passcode to a telephone (SMS text message or voice) number retrieved from the EHR. This reduced the risk of stolen EHR portal credentials. For a real-world test, we recruited 8 patients and (or) their caregivers who had EHR data at 2 independent health care facilities, enrolled them into Single-FILE, and allowed them to use their social identity credentials to access their patient records. We used a short qualitative interview to assess their interest and use of a federated identity for SSO. Single-FILE was implemented as a web-based patient portal, although the concept can be readily implemented on a variety of mobile platforms. RESULTS: We interviewed the patients and their caregivers to assess their comfort levels with using a social identity for access. Patients noted that they appreciated only having to remember 1 log-in as part of Single-FILE and being able to sign up through Facebook. CONCLUSIONS: Our results indicate that from a technical perspective, a social identity can be used as a federated identity that is bound to a patient's EHR data. The one-time passcode sent to the patient's EHR phone number provided assurance that the binding is valid. The patients indicated that they were comfortable with using their social credentials instead of having to remember the log-in credentials for their EHR portal. Our experience will help inform the implementation of federated identity systems in health care in the United States.

3.
Chest ; 160(4): 1222-1231, 2021 10.
Article in English | MEDLINE | ID: covidwho-1248852

ABSTRACT

BACKGROUND: The Hospitalization or Outpatient Management of Patients With SARS-CoV-2 Infection (HOME-CoV) rule is a checklist of eligibility criteria for home treatment of patients with COVID-19, defined using a Delphi method. RESEARCH QUESTION: Is the HOME-CoV rule reliable for identifying a subgroup of COVID-19 patients with a low risk of adverse outcomes who can be treated at home safely? STUDY DESIGN AND METHODS: We aimed to validate the HOME-CoV rule in a prospective, multicenter study before and after trial of patients with probable or confirmed COVID-19 who sought treatment at the ED of 34 hospitals. The main outcome was an adverse evolution, that is, invasive ventilation or death, occurring within the 7 days after patient admission. The performance of the rule was assessed by the false-negative rate. The impact of the rule implementation was assessed by the absolute differences in the rate of patients who required invasive ventilation or who died and in the rate of patients treated at home, between an observational and an interventional period after implementation of the HOME-CoV rule, with propensity score adjustment. RESULTS: Among 3,000 prospectively enrolled patients, 1,239 (41.3%) demonstrated a negative HOME-CoV rule finding. The false-negative rate of the HOME-CoV rule was 4 in 1,239 (0.32%; 95% CI, 0.13%-0.84%), and its area under the receiver operating characteristic curve was 80.9 (95% CI, 76.5-85.2). On the adjusted populations, 25 of 1,274 patients (1.95%) experienced an adverse evolution during the observational period vs 12 of 1,274 patients (0.95%) during the interventional period: -1.00 (95% CI, -1.86 to -0.15). During the observational period, 858 patients (67.35%) were treated at home vs 871 patients (68.37%) during the interventional period: -1.02 (95% CI, -4.46 to 2.26). INTERPRETATION: A large proportion of patients treated in the ED with probable or confirmed COVID-19 have a negative HOME-CoV rule finding and can be treated safely at home with a very low risk of complications. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT04338841; URL: www.clinicaltrials.gov.


Subject(s)
Ambulatory Care/methods , COVID-19/therapy , Decision Support Systems, Clinical , Disease Management , Hospitalization/trends , Outpatients , SARS-CoV-2 , Female , Humans , Male , Middle Aged , Patient Discharge/trends
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