Evidence map›Paper›PMID 38443959›Full record

ArticleBMC health services research2024

Cross-sectional analysis of primary care clinics' policies, practices, and availability of patient support services during the COVID-19 pandemic.

Kendra L Ratnapradipa, Runqiu Wang, Josiane Kabayundo, Walter Marquez Lavenant, Eleanore Nelson, Muskan Ahuja, Ying Zhang, Hongmei Wang

Abstract read
In one paragraph

Article in BMC health services research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Methods and lessons from conducting a national study of primary care nurse practitioners.Journal of the American Association of Nurse Practitioners · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Kendra L RatnapradipaDepartment of Epidemiology, University of Nebraska Medical Center, 984395 Nebraska Medical Center, Omaha, NE, 68198-4395, USA. Kendra.ratnapradipa@unmc.edu.ORCID http://orcid.org/0000-0003-0265-2400
Runqiu WangDepartment of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA.
Josiane KabayundoDepartment of Epidemiology, University of Nebraska Medical Center, 984395 Nebraska Medical Center, Omaha, NE, 68198-4395, USA.
Walter Marquez LavenantDepartment of Epidemiology, University of Nebraska Medical Center, 984395 Nebraska Medical Center, Omaha, NE, 68198-4395, USA.
Eleanore NelsonDepartment of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA.
Muskan AhujaDepartment of Epidemiology, University of Nebraska Medical Center, 984395 Nebraska Medical Center, Omaha, NE, 68198-4395, USA.
Ying ZhangDepartment of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA.
Hongmei WangDepartment of Health Services Research & Administration, University of Nebraska Medical Center, Omaha, NE, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealthcare accessibility and utilization are important social determinants of health. Lack of access to healthcare, including missed or no-show appointments, can have negative health effects and be costly to patients and providers. Various office-based approaches and community partnerships can address patient access barriers.

objectives(1) To understand provider perceptions of patient barriers; (2) to describe the policies and practices used to address late or missed appointments, and (3) to evaluate access to patient support services, both in-clinic and with community partners.

methodsMailed cross-sectional survey with online response option, sent to all Nebraska primary care clinics (n = 577) conducted April 2020 and January through April 2021. Chi-square tests compared rural-urban differences; logistic regression of clinical factors associated with policies and support services computed odds ratios (OR) and 95% confidence intervals (CI).

resultsResponse rate was 20.3% (n = 117), with 49 returns in 2020. Perceived patient barriers included finances, higher among rural versus urban clinics (81.6% vs. 56.1%, p =.009), and time (overall 52.3%). Welcoming environment (95.5%), telephone appointment reminders (74.8%) and streamlined admissions (69.4%) were the top three clinic practices to reduce missed appointments. Telehealth was the most commonly available patient support service in rural (79.6%) and urban (81.8%, p =.90) clinics. Number of providers was positively associated with having a patient navigator/care coordinator (OR = 1.20, CI = 1.02-1.40). For each percent increase in the number of privately insured patients, the odds of providing legal aid decreased by 4% (OR = 0.96, CI = 0.92-1.00). Urban clinics were less likely than rural clinics to provide social work services (OR = 0.16, CI = 0.04-0.67) or assist with applications for government aid (OR = 0.22, CI = 0.06-0.90).

conclusionsPractices to reduce missed appointments included a variety of reminders. Although finances and inability to take time off work were the most frequently reported perceived barriers for patients' access to timely healthcare, most clinics did not directly address them. Rural clinics appeared to have more community partnerships to address underlying social determinants of health, such as transportation and assistance applying for government aid. Taking such a wholistic partnership approach is an area for future study to improve patient access.

Indexed as

COVID-19Ambulatory Care FacilitiesCross-Sectional StudiesHumansPandemicsPolicyPrimary Health CareAccessibilityCOVID-19Health care/services“no show” or missed appointmentsRural-urban disparities

Identifiers

PMID38443959
PMCPMC10916250

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.