Opportunities and Design Considerations for Peer Support in a Hospital Setting

Haldar, Shefali; Mishra, Sonali R.; Khelifi, Maher; Pollack, Ari H.; Pratt, Wanda

doi:10.1145/3025453.3026040

Cited by 27 publications

(11 citation statements)

References 41 publications

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“…Online peer support interventions implemented in hospital settings have also been identified to maintain wellbeing, providing a forum to gather information about features of medical care, learn more about providers of different health or social care, exchange emotional support or manage time in the hospital (Haldar et al, 2017). Given the amount of time people on haemodialysis spend in hospital receiving renal replacement therapy, the use of online approaches is considered feasible, but only for people who are computer literate with milder emotional difficulties (Hudson et al, 2017).…”

Section: Meta-competenciesmentioning

confidence: 99%

Development of a competency framework for the Assistant Wellbeing Practitioner (Renal) role

Farrand

Hamilton

Strickland

2022

Journal of Renal Care

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Background: Many people with kidney disease experience comorbid mental health difficulties that result in worse physical health outcomes alongside greater personal, treatment and societal costs. Problem: Workforce expansion to treat comorbid mental health difficulties has focussed on psychological practitioners. This fails to capitalise on benefits arising from embedding roles to address biopsychosocial outcomes directly within the renal specialty. A competency framework to inform development and training for such a role has not been developed. Methods: Five-phase process to develop a competency framework for an Assistant Wellbeing Practitioner (Renal) role. Following identification of competency frameworks for roles in psychological practice, health and social care, relevant competencies were synthesised to create a draft competency framework. This framework was revised through consultation events with professionals associated with the renal specialty and Kidney Patient Involvement Network with the framework informing a competency map. Results: The competency map comprised three categories-Knowledge, Values and Principles, Core Skills and Meta-Competencies with specific competencies for an assistant practitioner role to work within the renal specialty identified. Core knowledge and skills included awareness of kidney treatments and common psychosocial difficulties, collaborative care and supporting evidence-based prevention approaches. Conclusions: Competencies associated with the Assistant Wellbeing Practitioner (Renal) role have the potential to promote mental wellbeing, better physical health and generate social and economic benefits. The competency map can inform training and role evaluation, although addressing implementation issues associated with commissioning physical and mental healthcare is required.

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Section: Meta-competenciesmentioning

confidence: 99%

Development of a competency framework for the Assistant Wellbeing Practitioner (Renal) role

Farrand

Hamilton

Strickland

2022

Journal of Renal Care

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“…Researchers have begun to explore new ways to inform inpatients about their care [4,19,38,45,50,51,53,54,56], as well as to understand how patients and caregivers work together to exchange care-related information [23,39]. However, between receiving information and communicating about it lies a crucial step: tracking information.…”

Section: Related Workmentioning

confidence: 99%

Supporting Collaborative Health Tracking in the Hospital

Mishra

Miller

Haldar

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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The hospital setting creates a high-stakes environment where patients’ lives depend on accurate tracking of health data. Despite recent work emphasizing the importance of patients’ engagement in their own health care, less is known about how patients track their health and care in the hospital. Through interviews and design probes, we investigated hospitalized patients’ tracking activity and analyzed our results using the stage-based personal informatics model. We used this model to understand how to support the tracking needs of hospitalized patients at each stage. In this paper, we discuss hospitalized patients’ needs for collaboratively tracking their health with their care team. We suggest future extensions of the stage-based model to accommodate collaborative tracking situations, such as hospitals, where data is collected, analyzed, and acted on by multiple people. Our findings uncover new directions for HCI research and highlight ways to support patients in tracking their care and improving patient safety.

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“…Understanding the needs of patients is an important thing that affects patient satisfaction. Patients who are satisfied is a very valuable asset, because the patient satisfaction will provide information to people close (relatives, family and neighbor) [3], that the services provided by the public hospitals very satisfying. physician practices, pharmacies and others, are also part of the output of hospital services required to foster good cooperation and mutual benefit with emphasis on patient satisfaction [4].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Single Exponential Smoothing and Holt's Method for Quality of Hospital Services Forecasting in General Hospital

Rachmat

Suhartono

2020

Bulletin of Comp. Sci. & Electr. Eng.

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The quality health service is one of the basic necessities of any person or customer. To predict the number of goods can be done in a way predicted. The comparison method of Single Exponential Smoothing and Holt's method is used to predict the accuracy of inpatient services will be back for the coming period. Single Exponential Smoothing the forecasting methods used for data stationary or data is relatively stable. Holt's method is used to test for a trend or data that has a tendency to increase or decrease in the long term. The outcome of this study is the Single Exponential Smoothing method is more precise than Holt's method because of the history of hospitalized patients who do not experience an increase or no trend. In addition, the percentage of error (the difference between the actual data with the forecast value) and Mean Absolute Deviation (MAD) to calculate the forecast error obtained from the Single Exponential Smoothing method is smaller compared to Holt's method.

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Opportunities and Design Considerations for Peer Support in a Hospital Setting

Cited by 27 publications

References 41 publications

Development of a competency framework for the Assistant Wellbeing Practitioner (Renal) role

Development of a competency framework for the Assistant Wellbeing Practitioner (Renal) role

Supporting Collaborative Health Tracking in the Hospital

Comparative Analysis of Single Exponential Smoothing and Holt's Method for Quality of Hospital Services Forecasting in General Hospital

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