About

Siu Chiu Chan, PhD, is an Assistant Professor of Medicine in the Division of Nephrology & Hypertension at the Renaissance School of Medicine, Stony Brook University. His primary research focuses on studying the role of dysregulated signaling pathways in the initiation and progression of Polycystic Kidney Disease (PKD). He analyzes dysregulated transcriptional mechanisms in PKD using cell line models, mouse models, and human PKD patient samples. His work employs genomic engineering, molecular biology, biochemical methods, and genomic and proteomic tools to investigate aberrant transcriptional components mediating signal transduction pathways in PKD, with the aim of developing targeted therapeutic approaches to prevent cyst formation and progression.

Research topics

• Computer Science
• Medicine
• Psychology
• Nursing
• Family medicine
• Psychiatry
• Political Science
• Medical education
• Internal medicine
• Computer network
• Applied psychology

Selected publications

• Asynchronous Technologies in Mental Health Care and Education

  Current Treatment Options in Psychiatry · 2023-05-04 · 15 citations

  reviewOpen access
  PublisherOA PDFDOI

• Patient and Provider Satisfaction with Asynchronous Versus Synchronous Telepsychiatry in Primary Care: A Secondary Mixed-Methods Analysis of a Randomized Controlled Trial

  Telemedicine Journal and e-Health · 2023-11-27 · 4 citations

  articleOpen access

  Background: Asynchronous telepsychiatry (ATP) consultations are a novel form of psychiatric consultation. Studies comparing patient and provider satisfaction for ATP with that for synchronous telepsychiatry (STP) do not exist. Methods: This mixed-methods study is a secondary analysis of patients' and primary care providers' (PCPs) satisfaction from a randomized clinical trial of ATP compared with STP. Patients and their PCPs completed satisfaction surveys, and provided unstructured feedback about their experiences with either ATP or STP. Differences in patient satisfaction were assessed using mixed-effects logistic regression models, and the qualitative data were analyzed using thematic analysis with an inductive coding framework. Results: Patient satisfaction overall was high with 84% and 97% of respondents at 6 months reported being somewhat or completely satisfied with ATP and STP, respectively. Patients in the STP group were more likely to report being completely satisfied, to recommend the program to a friend, and to report being comfortable with their care compared with ATP (all p < 0.05). However, there was no difference between the patients in ATP and STP in perceived change in clinical outcomes ( p = 0.51). The PCP quantitative data were small, and thus only summarized descriptively. Conclusions: Patients expressed their overall satisfaction with both STP and ATP. Patients in ATP reported more concerns about the process, likely because feedback after ATP was slower than that after STP consultations. PCPs had no apparent preference for STP or ATP, and reported implementing the psychiatrists' recommendations for both groups when such recommendations were made, which supports our previous findings. Trial Registration: ClinicalTrials.gov NCT02084979; https://clinicaltrials.gov/ct2/show/NCT02084979.

  PublisherOA PDFDOI

• The Use of Automated Machine Translation to Translate Figurative Language in a Clinical Setting: Analysis of a Convenience Sample of Patients Drawn From a Randomized Controlled Trial

  JMIR Mental Health · 2022-07-11 · 13 citations

  articleOpen access

  BACKGROUND: Patients with limited English proficiency frequently receive substandard health care. Asynchronous telepsychiatry (ATP) has been established as a clinically valid method for psychiatric assessments. The addition of automated speech recognition (ASR) and automated machine translation (AMT) technologies to asynchronous telepsychiatry may be a viable artificial intelligence (AI)-language interpretation option. OBJECTIVE: This project measures the frequency and accuracy of the translation of figurative language devices (FLDs) and patient word count per minute, in a subset of psychiatric interviews from a larger trial, as an approximation to patient speech complexity and quantity in clinical encounters that require interpretation. METHODS: A total of 6 patients were selected from the original trial, where they had undergone 2 assessments, once by an English-speaking psychiatrist through a Spanish-speaking human interpreter and once in Spanish by a trained mental health interviewer-researcher with AI interpretation. 3 (50%) of the 6 selected patients were interviewed via videoconferencing because of the COVID-19 pandemic. Interview transcripts were created by automated speech recognition with manual corrections for transcriptional accuracy and assessment for translational accuracy of FLDs. RESULTS: AI-interpreted interviews were found to have a significant increase in the use of FLDs and patient word count per minute. Both human and AI-interpreted FLDs were frequently translated inaccurately, however FLD translation may be more accurate on videoconferencing. CONCLUSIONS: AI interpretation is currently not sufficiently accurate for use in clinical settings. However, this study suggests that alternatives to human interpretation are needed to circumvent modifications to patients' speech. While AI interpretation technologies are being further developed, using videoconferencing for human interpreting may be more accurate than in-person interpreting. TRIAL REGISTRATION: ClinicalTrials.gov NCT03538860; https://clinicaltrials.gov/ct2/show/NCT03538860.

  PublisherOA PDFDOI

• Findings and Guidelines on Provider Technology, Fatigue, and Well-being: Scoping Review

  Journal of Medical Internet Research · 2022-04-18 · 51 citations

  articleOpen access

  BACKGROUND: Video and other technologies are reshaping the delivery of health care, yet barriers related to workflow and possible provider fatigue suggest that a thorough evaluation is needed for quality and process improvement. OBJECTIVE: This scoping review explored the relationship among technology, fatigue, and health care to improve the conditions for providers. METHODS: A 6-stage scoping review of literature (from 10 databases) published from 2000 to 2020 that focused on technology, health care, and fatigue was conducted. Technologies included synchronous video, telephone, informatics systems, asynchronous wearable sensors, and mobile health devices for health care in 4 concept areas related to provider experience: behavioral, cognitive, emotional, and physical impact; workplace at the individual, clinic, hospital, and system or organizational levels; well-being, burnout, and stress; and perceptions regarding technology. Qualitative content, discourse, and framework analyses were used to thematically analyze data for developing a spectrum of health to risk of fatigue to manifestations of burnout. RESULTS: Of the 4221 potential literature references, 202 (4.79%) were duplicates, and our review of the titles and abstracts of 4019 (95.21%) found that 3837 (90.9%) were irrelevant. A full-text review of 182 studies revealed that 12 (6.6%) studies met all the criteria related to technology, health care, and fatigue, and these studied the behavioral, emotional, cognitive, and physical impact of workflow at the individual, hospital, and system or organizational levels. Video and electronic health record use has been associated with physical eye fatigue; neck pain; stress; tiredness; and behavioral impacts related to additional effort owing to barriers, trouble with engagement, emotional wear and tear and exhaustion, cognitive inattention, effort, expecting problems, multitasking and workload, and emotional experiences (eg, anger, irritability, stress, and concern about well-being). An additional 14 studies that evaluated behavioral, emotional, and cognitive impacts without focusing on fatigue found high user ratings on data quality, accuracy, and processing but low satisfaction with clerical tasks, the effort required in work, and interruptions costing time, resulting in more errors, stress, and frustration. Our qualitative analysis suggests a spectrum from health to risk and provides an outline of organizational approaches to human factors and technology in health care. Business, occupational health, human factors, and well-being literature have not studied technology fatigue and burnout; however, their findings help contextualize technology-based fatigue to suggest guidelines. Few studies were found to contextually evaluate differences according to health professions and practice contexts. CONCLUSIONS: Health care systems need to evaluate the impact of technology in accordance with the Quadruple Aim to support providers' well-being and prevent workload burden, fatigue, and burnout. Implementation and effectiveness approaches and a multilevel approach with objective measures for clinical, human factors, training, professional development, and administrative workflow are suggested. This requires institutional strategies and competencies to integrate health care quality, technology and well-being outcomes.

  PublisherOA PDFDOI

• Practical Considerations for Emerging Types of Telebehavioral Health Care: Computer-Assisted Cognitive Behavior Therapy and Mobile Applications

  2022-01-01 · 1 citations

  book-chapter
  PublisherDOI

• Integrating In-Person, Video, and Asynchronous Technologies in Rural Primary Care

  2022-01-01 · 1 citations

  book-chapterSenior author
  PublisherDOI

• Is Automated Machine Translation sufficiently accurate to translate figurative language in a clinical setting? (Preprint)

  2022-05-13

  preprintOpen access

  <sec> <title>BACKGROUND</title> Patients with Limited English Proficiency (LEP) frequently receive substandard healthcare. Asynchronous Telepsychiatry (ATP) has been established as a clinically valid method for psychiatric assessments (1). The addition of automated speech recognition (ASR) and automated machine translation (AMT) technologies to ATP may be a viable Artificial Intelligence (AI)-language interpretation option. </sec> <sec> <title>OBJECTIVE</title> This project measured the frequency and accuracy of translation of figurative language devices (FLDs), and the patient word count per minute in a subset of psychiatric interviews from a larger trial, as an approximation to patient speech complexity and quantity in clinical encounters requiring interpretation. </sec> <sec> <title>METHODS</title> Six patients were selected from the original trial, where they had undergone two assessments, once by an English-speaking psychiatrist through a Spanish-speaking human interpreter and once in Spanish by a trained mental health interviewer-researcher with AI-interpretation. Three patients were interviewed via videoconferencing because of the COVID-19 pandemic. Interview transcripts were created by ASR with manual corrections for transcriptional accuracy and assessment for translational accuracy of FLDs. </sec> <sec> <title>RESULTS</title> Both human and AI-interpreted FLDs were frequently translated inaccurately, while human-interpreted interviews were found to have a significant reduction in the use of FLDs and the patient word count per minute; FLD translation was more accurate on videoconferencing. </sec> <sec> <title>CONCLUSIONS</title> AI-interpretation is not sufficiently accurate at this time for use in clinical settings. However, this study suggests that alternatives to human interpretation are needed to circumvent modifications to patients’ speech. While AI-interpretation technologies are being further developed, using videoconferencing for human interpreting may be more accurate than in-person interpreting. </sec> <sec> <title>CLINICALTRIAL</title> Clinicaltrials.gov NCT03538860; https://clinicaltrials.gov/ct2/show/NCT03538860 </sec>

  PublisherDOI

• Primary Care Physician Adherence to Telepsychiatry Recommendations: Intermediate Outcomes from a Randomized Clinical Trial

  Telemedicine Journal and e-Health · 2021 · 8 citations

  • Medicine
  • Family medicine
  • Internal medicine

  .

  PublisherOA PDFDOI

• Asynchronous Telepsychiatry Interviewer Training Recommendations: A Model for Interdisciplinary, Integrated Behavioral Health Care

  Telemedicine Journal and e-Health · 2021 · 8 citations

  • Computer Science
  • Medical education
  • Psychology

  We describe and provide a suggested training model for the use of ATP integrated behavioral health. The training needs for ATP clinicians were assessed on a limited convenience sample of experts and clinicians, and more rigorous studies of training for ATP and other technology-focused, behavioral health services are needed. Clinical Trials number: NCT03538860.

  PublisherOA PDFDOI

• NODE.Health Algorithm to Support Digital Mental Health Validation

  Oxford University Press eBooks · 2021-01-01

  book-chapter

  Over the last several years, there has been rapid growth of digital technologies attempting to transform healthcare. Unique features of digital medicine technology lead to both challenges and opportunities for testing and validation. Yet little guidance exists to help a health system decide whether to undertake a pilot test of new technology, move right to full-scale adoption, or start somewhere in between. To navigate this complexity, this chapter proposes an algorithm to help choose the best path toward validation and adoption. Special attention is paid to considering whether the needs of patients with limited digital skills, equipment (e.g., smartphones) and connectivity (e.g., data plans) have been considered in technology development and deployment. The algorithm reflects the collective experience of 20+ health systems and academic institutions that have established the Network of Digital Evidence for Health, NODE.Health, plus insights from existing clinical research taxonomies, syntheses, or frameworks for assessing technology or for reporting clinical trials.

  PublisherDOI

Frequent coauthors

• John Torous

  Harvard University

  98 shared

• Donald M. Hilty

  50 shared

• Peter Yellowlees

  University of California, Davis

  41 shared

• David Gratzer

  Centre for Addiction and Mental Health

  34 shared

• John Luo

  National Patient Safety Foundation

  26 shared

• Patrick Aquino

  Lahey Medical Center

  25 shared

• Sarah Lagan

  University of California, San Diego

  25 shared

• Michelle Dirst

  The University of Texas Southwestern Medical Center

  25 shared

Education

• Fellow, Clinical Informatics Fellowship

  University of California San Francisco Division of Hospital Medicine

  2018

• Resident, General Adult Psychiatry Residency

  University of California Davis School of Medicine

  2016

• Doctor of Medicine, Medicine with Distinction in Service

  University of California Irvine School of Medicine

  2012

• MBA, Business Administration, Emphasis in Healthcare Administration

  University of California Irvine Paul Merage School of Business

  2012

Awards & honors

• Selected as a 2015 “Breakthrough Article” by NAR