scholarly journals Lessons Learned: Conducting Cases of Manualized, Telephone-Based, Cognitive Behavioral Treatment for Depression in Parkinson’s Disease (dPD)

2020 ◽  
Vol 16 (1) ◽  
pp. 124-131
Author(s):  
Logan Durland
Keyword(s):  
Parkinson’S Disease ◽  
Decision Making ◽  
Parkinson's Disease ◽  
Case Studies ◽  
Behavioral Treatment ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Clinical Population ◽  
Recent Experience

My current clinical practice has been shifted to a telehealth format for the last three months due to the COVID-19 pandemic, and it seems an apt moment to reexamine my participation in Dr. Roseanne Dobkin’s research on manualized telehealth therapy for depression in Parkinson’s disease patients (dPD), using a protocol titled "Teleheath Guided Self-Help for dPD," or "TH-GSH-dPD," for short (Dobkin et al., 2020). My participation involved, in part, being the therapist in  four case studies I have written about with "Alice," "Carl," "Ethan," and "Gary" (Durland, 2020). In these case studies, a subset of those in Dr. Dobkin’s group studies, I explored my clinical decision-making, seeking insight into how best to flexibly apply the dPD protocol to meet the needs of a heterogeneous clinical population. Here, my aim is to recontextualize and expand on the conclusions of my four case studies, based on my dissertation and conducted over three years ago (Durland, 2017), in light of both my recent experience providing mental health services and the Commentaries on the four case studies so perceptively contributed by Dr. Dobkin and her colleagues (Mann, Miller, St. Hill, Dobkin, 2020) and by Liza Pincus (2020). In particular, I will focus first on (a) continuing the analysis of clinical decision-making involved in the case studies described in my earlier article (Duland, 2020); and then on (b) general issues related to the delivery of telehealth treatment.

HEALTH TECHNOLOGY ASSESSMENT AND CLINICAL DECISION MAKING:

1999 ◽  
Vol 15 (3) ◽  
pp. 585-592 ◽  
Author(s):  
Alicia Granados
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Technology Assessment ◽  
Case Studies ◽  
Technology Assessment ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Health Technology ◽  
Evidence Based ◽  
Systematic Process

This paper examines the rationality of the concepts underlying evidence—based medicineand health technology assessment (HTA), which are part of a new current aimed at promoting the use of the results of scientific studies for decision making in health care. It describes the different approaches and purposes of this worldwide movement, in relation to clinical decision making, through a summarized set of specific HTA case studies from Catalonia, Spain. The examples illustrate how the systematic process of HTA can help in several types of uncertainties related to clinical decision making.

scholarly journals Integrating Patient-Generated Health Data Into Clinical Care Settings or Clinical Decision-Making: Lessons Learned From Project HealthDesign

2016 ◽  
Vol 3 (2) ◽  
pp. e26 ◽  
Author(s):  
Deborah J Cohen ◽  
Sara R Keller ◽  
Gillian R Hayes ◽  
David A Dorr ◽  
Joan S Ash ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Clinical Decision ◽  
Health Data ◽  
Lessons Learned

scholarly journals Clinical Informatics during the COVID-19 Pandemic: Lessons Learned and Implications for Emergency Department and Inpatient Operations

2020 ◽  
Author(s):  
Hanson Hsu ◽  
Peter W Greenwald ◽  
Matthew R Laghezza ◽  
Peter Steel ◽  
Richard Trepp ◽  
...  
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Medical Care ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Clinical Informatics ◽  
Security And Privacy ◽  
Care Pathways ◽  
Provider Education

Abstract In response to a pandemic, hospital leaders can use clinical informatics to aid clinical decision-making, virtualizing medical care, coordinating communication, and defining workflow and compliance. Clinical informatics procedures need to be implemented nimbly, with governance measures in place to properly oversee and guide novel patient care pathways, diagnostic and treatment workflows, and provider education and communication. The authors’ experience recommends: (1) creating flexible ordersets that adapt to evolving guidelines that meet needs across specialties (2) enhancing and supporting inherent telemedicine capability (3) electronically enabling novel workflows quickly and suspending non-critical administrative or billing functions in the EHR and (4) using communication platforms based on tiered urgency that do not compromise security and privacy.

scholarly journals Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review

Informatics ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 16
Author(s):  
Mahanazuddin Syed ◽  
Shorabuddin Syed ◽  
Kevin Sexton ◽  
Hafsa Bareen Syeda ◽  
Maryam Garza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Intensive Care ◽  
Clinical Decision Making ◽  
Medical Information ◽  
Descriptive Analysis ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Quantitative Descriptive Analysis ◽  
Quantitative Descriptive

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.

Sign in / Sign up

Export Citation Format

Share Document