Care partners (CP) of people with dementia (PWD) report that decisions about care setting are aided by the support of healthcare providers. However, providers are often underprepared to offer adequate counseling. This qualitative study aimed to identify what support from providers will assist CPs in making decisions related to care setting throughout the dementia journey. We conducted semi-structured interviews with current CPs of PWD and former CPs of decedents. We utilized the constant comparative method to identify themes regarding preferences around care setting as the PWD progressed from diagnosis to end-of-life. Participants were 31 CPs, including 16 current and 15 former CPs. CPs had a mean age of 67 and were primarily white (n = 23/31), female (n = 21/31), and spouses (n = 24/31). Theme 1: Current CPs discussed overwhelming uncertainty pertaining to care setting, expressing I dont know when I need to plan on more care, and a desire to understand what stage we are at. Theme 2: Later in the disease, former CPs wanted guidance from healthcare providers on institutional placement (I sure wouldve loved some help finding better places) or support to stay in the home (a doctor had to come to the house). CPs want early, specific guidance from healthcare providers related to transitions between home and long-term care. Early in the disease course, counseling geared toward prognosis and expected disease course helps CPs make plans. Later, caregivers want help identifying locations or institutionalization or finding home care resources.

BACKGROUND:Diagnostic error in ambulatory care, a frequent cause of preventable harm, may be mitigated using the collective intelligence of multiple clinicians. The National Academy of Medicine has identified enhanced clinician collaboration and digital tools as a means to improve the diagnostic process. OBJECTIVE:This study aims to assess the efficacy of a collective intelligence output to improve diagnostic confidence and accuracy in ambulatory care cases (from primary care and urgent care clinic visits) with diagnostic uncertainty. METHODS:This is a pragmatic randomized controlled trial of using collective intelligence in cases with diagnostic uncertainty from clinicians at primary care and urgent care clinics in 2 health care systems in San Francisco. Real-life cases, identified for having an element of diagnostic uncertainty, will be entered into a collective intelligence digital platform to acquire collective intelligence from at least 5 clinician contributors on the platform. Cases will be randomized to an intervention group (where clinicians will view the collective intelligence output) or control (where clinicians will not view the collective intelligence output). Clinicians will complete a postvisit questionnaire that assesses their diagnostic confidence for each case; in the intervention cases, clinicians will complete the questionnaire after reviewing the collective intelligence output for the case. Using logistic regression accounting for clinician clustering, we will compare the primary outcome of diagnostic confidence and the secondary outcome of time with diagnosis (the time it takes for a clinician to reach a diagnosis), for intervention versus control cases. We will also assess the usability and satisfaction with the digital tool using measures adapted from the Technology Acceptance Model and Net Promoter Score. RESULTS:We have recruited 32 out of our recruitment goal of 33 participants. This study is funded until May 2020 and is approved by the University of California San Francisco Institutional Review Board until January 2020. We have completed data collection as of June 2019 and will complete our proposed analysis by December 2019. CONCLUSIONS:This study will determine if the use of a digital platform for collective intelligence is acceptable, useful, and efficacious in improving diagnostic confidence and accuracy in outpatient cases with diagnostic uncertainty. If shown to be valuable in improving clinicians' diagnostic process, this type of digital tool may be one of the first innovations used for reducing diagnostic errors in outpatient care. The findings of this study may provide a path forward for improving the diagnostic process. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):DERR1-10.2196/13151.

Background

Diagnostic error in ambulatory care, a frequent cause of preventable harm, may be mitigated using the collective intelligence of multiple clinicians. The National Academy of Medicine has identified enhanced clinician collaboration and digital tools as a means to improve the diagnostic process.

Objective

This study aims to assess the efficacy of a collective intelligence output to improve diagnostic confidence and accuracy in ambulatory care cases (from primary care and urgent care clinic visits) with diagnostic uncertainty.

Methods

This is a pragmatic randomized controlled trial of using collective intelligence in cases with diagnostic uncertainty from clinicians at primary care and urgent care clinics in 2 health care systems in San Francisco. Real-life cases, identified for having an element of diagnostic uncertainty, will be entered into a collective intelligence digital platform to acquire collective intelligence from at least 5 clinician contributors on the platform. Cases will be randomized to an intervention group (where clinicians will view the collective intelligence output) or control (where clinicians will not view the collective intelligence output). Clinicians will complete a postvisit questionnaire that assesses their diagnostic confidence for each case; in the intervention cases, clinicians will complete the questionnaire after reviewing the collective intelligence output for the case. Using logistic regression accounting for clinician clustering, we will compare the primary outcome of diagnostic confidence and the secondary outcome of time with diagnosis (the time it takes for a clinician to reach a diagnosis), for intervention versus control cases. We will also assess the usability and satisfaction with the digital tool using measures adapted from the Technology Acceptance Model and Net Promoter Score.

Results

We have recruited 32 out of our recruitment goal of 33 participants. This study is funded until May 2020 and is approved by the University of California San Francisco Institutional Review Board until January 2020. We have completed data collection as of June 2019 and will complete our proposed analysis by December 2019.

Conclusions

This study will determine if the use of a digital platform for collective intelligence is acceptable, useful, and efficacious in improving diagnostic confidence and accuracy in outpatient cases with diagnostic uncertainty. If shown to be valuable in improving clinicians' diagnostic process, this type of digital tool may be one of the first innovations used for reducing diagnostic errors in outpatient care. The findings of this study may provide a path forward for improving the diagnostic process.

International registered report identifier (irrid)

DERR1-10.2196/13151.

INTRODUCTION:Cervical cancer prevention is possible through use of the HPV vaccine and Pap tests, yet the vaccine remains underutilized. METHODS:We obtained publicly-available Twitter data from 2014 using three sampling strategies (top-ranked, simple random sample, and topic model) based on key words related to cervical cancer prevention. We conducted a content analysis of 100 tweets from each of the three samples and examined the extent to which the narratives and frequency of themes differed across samples. RESULTS:Advocacy-related tweets constituted the most prevalent theme to emerge across all three sample types, and were most frequently found in the top-ranked sample. A random sample detected the same themes as topic modeling, but the relative frequency of themes identified from topic modeling fell in-between top-ranked and random samples. DISCUSSION:Variations in themes uncovered by different sampling methods suggest it is useful to qualitatively assess the relative frequency of themes to better understand the breadth and depth of social media conversations about health. CONCLUSIONS:Future studies using social media data should consider sampling methods to uncover a wider breadth of conversations about health on social media.

Online social media platforms represent a promising opportunity for public health promotion. Research is limited, however, on the effectiveness of social media at improving knowledge and awareness of health topics and motivating healthy behavior change. Therefore, we investigated whether participation in an online social media platform and receipt of brief, tailored messages is effective at increasing knowledge, awareness, and prevention behaviors related to human papillomavirus (HPV) and cervical cancer. We conducted an online study in which 782 recruited participants were consecutively assigned to nine-person groups on a social media platform. Participants were shown a unique random set of 20 tailored messages per day over five days. Participants completed a baseline and post survey to assess their knowledge, awareness, and prevention behaviors related to HPV and cervical cancer. There were no statistically significant changes in knowledge and prevention behaviors from the baseline to the post survey among study participants. There was a modest, statistically significant change in response to whether participants had ever heard of HPV, increasing from 90 to 94% (p = 0.003). Our findings suggest that most study participants had substantial knowledge, awareness, and engagement in positive behaviors related to cervical cancer prevention at the start of the study. Nevertheless, we found that HPV awareness can be increased through brief participation in an online social media platform and receipt of tailored health messages. Further investigation that explores how social media can be used to improve knowledge and adoption of healthy behaviors related to cervical cancer is warranted.