Dataset Information

Differences in Stakeholders' Perception of the Impact of COVID-19 on Clinical Care and Decision-Making.

ABSTRACT:

Background

Pandemics are related to changes in clinical management. Factors that are associated with individual perceptions of related risks and decision-making processes focused on prevention and vaccination, but perceptions of other healthcare consequences are less investigated. Different perceptions of patients, nurses, and physicians on consequences regarding clinical management, decisional criteria, and burden were compared.

Study design

Cross-sectional OnCoVID questionnaire studies.

Methods

Data that involved 1231 patients, physicians, and nurses from 11 German institutions that were actively involved in clinical treatment or decision-making in oncology or psychiatry were collected. Multivariate statistical approaches were used to analyze the stakeholder comparisons.

Results

A total of 29.2% of professionals reported extensive changes in workload. Professionals in psychiatry returned severe impact of pandemic on all major aspects of their clinical care, but less changes were reported in oncology (p < 0.001). Both patient groups reported much lower recognition of treatment modifications and consequences for their own care. Decisional and pandemic burden was intensively attributed from professionals towards patients, but less in the opposite direction.

Conclusions

All of the groups share concerns about the impact of the COVID-19 pandemic on healthcare management and clinical processes, but to very different extent. The perception of changes is dissociated in projection towards other stakeholders. Specific awareness should avoid the dissociated impact perception between patients and professionals potentially resulting in impaired shared decision-making.

SUBMITTER: Haier J

PROVIDER: S-EPMC9454870 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Differences in Stakeholders' Perception of the Impact of COVID-19 on Clinical Care and Decision-Making.

Haier Joerg J Beller Johannes J Adorjan Kristina K Bleich Stefan S de Greck Moritz M Griesinger Frank F Heppt Markus V MV Hurlemann René R Mees Soeren Torge ST Philipsen Alexandra A Rohde Gernot G Schilling Georgia G Trautmann Karolin K Combs Stephanie E SE Geyer Siegfried S Schaefers Juergen J

Cancers 20220902 17

Background: Pandemics are related to changes in clinical management. Factors that are associated with individual perceptions of related risks and decision-making processes focused on prevention and vaccination, but perceptions of other healthcare consequences are less investigated. Different perceptions of patients, nurses, and physicians on consequences regarding clinical management, decisional criteria, and burden were compared. Study Design: Cross-sectional OnCoVID questionnaire studies. Meth ...[more]

PMID: 36077852

Similar Datasets

Project description:IntroductionLittle is known about the impact of mandated vaccination policies on the primary care clinic workforce in the United States or differences between rural and urban settings, especially for COVID-19. With the continued pandemic and an anticipated increase in novel disease outbreaks and emerging vaccines, healthcare systems need additional information on how vaccine mandates impact the healthcare workforce to aid in future decision-making.MethodsWe conducted a cross-sectional survey of Oregon primary care clinic staff between October 28, 2021- November 18, 2021, following implementation of a COVID-19 vaccination mandate for healthcare personnel. The survey consisted of 19 questions that assessed the clinic-level impacts of the vaccination mandate. Outcomes included job loss among staff, receipt of an approved vaccination waiver, new vaccination among staff, and the perceived significance of the policy on clinic staffing. We used univariable descriptive statistics to compare outcomes between rural and urban clinics. The survey also included three open-ended questions that were analyzed using a template analysis approach.ResultsStaff from 80 clinics across 28 counties completed surveys, representing 38 rural and 42 urban clinics. Clinics reported job loss (46%), use of vaccination waivers (51%), and newly vaccinated staff (60%). Significantly more rural clinics (compared to urban) utilized medical and/or religious vaccination waivers (71% vs 33%, p = 0.04) and reported significant impact on clinic staffing (45% vs 21%, p = 0.048). There was also a non-significant trend toward more job loss for rural compared to urban clinics (53% vs. 41%, p = 0.547). Qualitative analysis highlighted a decline in clinic morale, small but meaningful detriments to patient care, and mixed opinions of the vaccination mandate.ConclusionsOregon's COVID-19 vaccination mandate increased healthcare personnel vaccination rates, yet amplified staffing challenges with disproportionate impacts in rural areas. Staffing impacts in primary care clinics were greater than reported previously in hospital settings and with other vaccination mandates. Mitigating primary care staffing impacts, particularly in rural areas, will be critical in response to the continued pandemic and novel viruses in the future.

Project description:BackgroundElectronic medical records are widely used in family practices across Canada and can improve health outcomes. However, recent reports indicate that physicians using electronic medical records work longer and have less direct patient contact which may contribute to burnout. Therefore, new and innovative digital tools are essential to reduce physician workloads and improve patient-physician interaction to address physician burnout. The objective of this study was to assess the efficiency and accuracy of clinical decision-making when using a new preventive care point-of-care clinical decision support system (CDSS). An estimate of the potential annual time savings was also determined. This study also assessed physician reported perceived usefulness and ease of use of the CDSS.MethodsQuantitative and qualitative data were collected during this study. Each participant evaluated two simulated patient charts and identified which preventive care metrics were due. The participants recorded their decisions and the time required to assess each chart. Participants then completed a Technology Acceptance Model survey regarding the perceived usefulness and ease of use of the CDSS, which included qualitative feedback. The amount of time saved was determined and participants' clinical decision-making accuracy was scored against current Canadian preventive care guidelines. The number of preventive care specific visits completed per year was determined using clinic billing data.ResultsThe preventive care CDSS saved an average of 195.7 s of chart review time (249.5 s vs 445.2 s; P < 0.001). A total of 1520 preventive visits were performed at Primrose and Bruyère Family Medicine Centres. Extrapolated across the organization, implementation of the new tool could save 82.6 h per year. Decision-making accuracy was not affected by the new tool (78.4% vs 80.9%, P > 0.05). Participants rated the perceived ease of use and usefulness to be very high.ConclusionsNew digital tools may reduce providers' workload without impacting clinical decision-making accuracy. Participants indicated that the preventive care CDSS was useful and easy to use. Further software development and clinical studies are required to further improve and characterize the effect this new CDSS has when implemented in clinical practice.

Project description:BackgroundThe COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. This framework is also used as an informative, continuously enriched data lake, which is a base for several on-going predictive studies.MethodsThe information technology framework for clinical practice and research was described. It was developed using SAS Institute software analytics tool and SAS® Vyia® environment and Open-Source environment R ® and Python ® for fast prototyping and modeling. The included variables and the source extraction procedures were presented.ResultsThe Data Mart covers a retrospective cohort of 5528 patients with SARS-CoV-2 infection. People who died were older, had more comorbidities, reported more frequently dyspnea at onset, had higher d-dimer, C-reactive protein and urea nitrogen. The dashboard was developed to support the management of COVID-19 patients at three levels: hospital, single ward and individual care level.InterpretationThe COVID-19 Data Mart based on integration of a large collection of clinical data and an AI-based integrated framework has been developed, based on a set of automated procedures for data mining and retrieval, transformation and integration, and has been embedded in the clinical practice to help managing daily care. Benefits from the availability of a Data Mart include the opportunity to build predictive models with a machine learning approach to identify undescribed clinical phenotypes and to foster hospital networks. A real-time updated dashboard built from the Data Mart may represent a valid tool for a better knowledge of epidemiological and clinical features of COVID-19, especially when multiple waves are observed, as well as for epidemic and pandemic events of the same nature (e. g. with critical clinical conditions leading to severe pulmonary inflammation). Therefore, we believe the approach presented in this paper may find several applications in comparable situations even at region or state levels. Finally, models predicting the course of future waves or new pandemics could largely benefit from network of DataMarts.

Dataset Information

Differences in Stakeholders' Perception of the Impact of COVID-19 on Clinical Care and Decision-Making.

Background

Study design

Methods

Results

Conclusions

Publications

Differences in Stakeholders' Perception of the Impact of COVID-19 on Clinical Care and Decision-Making.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets