scholarly journals Health Information Obtained From the Internet and Changes in Medical Decision Making: Questionnaire Development and Cross-Sectional Survey (Preprint)

2017 ◽  
Author(s):  
Yen-Yuan Chen ◽  
Chia-Ming Li ◽  
Jyh-Chong Liang ◽  
Chin-Chung Tsai
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Health Information ◽  
Health Care Professionals ◽  
Medical Decision Making ◽  
Pearson Correlation ◽  
Medical Decision ◽  
The Internet ◽  
Online Health Information ◽  
Information Utilization

BACKGROUND The increasing utilization of the internet has provided a better opportunity for people to search online for health information, which was not easily available to them in the past. Studies reported that searching on the internet for health information may potentially influence an individual’s decision making to change her health-seeking behaviors. OBJECTIVE The objectives of this study were to (1) develop and validate 2 questionnaires to estimate the strategies of problem-solving in medicine and utilization of online health information, (2) determine the association between searching online for health information and utilization of online health information, and (3) determine the association between online medical help-seeking and utilization of online health information. METHODS The Problem Solving in Medicine and Online Health Information Utilization questionnaires were developed and implemented in this study. We conducted confirmatory factor analysis to examine the structure of the factor loadings and intercorrelations for all the items and dimensions. We employed Pearson correlation coefficients for examining the correlations between each dimension of the Problem Solving in Medicine questionnaire and each dimension of the Online Health Information Utilization questionnaire. Furthermore, we conducted structure equation modeling for examining the possible linkage between each of the 6 dimensions of the Problem Solving in Medicine questionnaire and each of the 3 dimensions of the Online Health Information Utilization questionnaire. RESULTS A total of 457 patients participated in this study. Pearson correlation coefficients ranged from .12 to .41, all with statistical significance, implying that each dimension of the Problem Solving in Medicine questionnaire was significantly associated with each dimension of the Online Health Information Utilization questionnaire. Patients with the strategy of online health information search for solving medical problems positively predicted changes in medical decision making (P=.01), consulting with others (P<.001), and promoting self-efficacy on deliberating the online health information (P<.001) based on the online health information they obtained. CONCLUSIONS Present health care professionals have a responsibility to acknowledge that patients’ medical decision making may be changed based on additional online health information. Health care professionals should assist patients’ medical decision making by initiating as much dialogue with patients as possible, providing credible and convincing health information to patients, and guiding patients where to look for accurate, comprehensive, and understandable online health information. By doing so, patients will avoid becoming overwhelmed with extraneous and often conflicting health information. Educational interventions to promote health information seekers’ ability to identify, locate, obtain, read, understand, evaluate, and effectively use online health information are highly encouraged.

May We Support Your Decision?

1996 ◽  
Vol 1 (3) ◽  
pp. 175-178 ◽  
Author(s):  
Colin Gordon
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Making ◽  
Clinical Practice ◽  
Decision Support ◽  
Health Care Professionals ◽  
Medical Decision Making ◽  
Electronic Patient Record ◽  
Medical Decision ◽  
Technical Developments

Expert systems to support medical decision-making have so far achieved few successes. Current technical developments, however, may overcome some of the limitations. Although there are several theoretical currents in medical artificial intelligence, there are signs of them converging. Meanwhile, decision support systems, which set themselves more modest goals than replicating or improving on clinicians' expertise, have come into routine use in places where an adequate electronic patient record exists. They may also be finding a wider role, assisting in the implementation of clinical practice guidelines. There is, however, still much uncertainty about the kinds of decision support that doctors and other health care professionals are likely to want or accept.

The role of the Internet in medical decision making

1997 ◽  
Vol 47 (1-2) ◽  
pp. 43-49 ◽  
Author(s):  
M.F. Anderson ◽  
H. Moazamipour ◽  
D.L. Hudson ◽  
M.E. Cohen
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Medical Decision ◽  
The Internet

The usage of social media encouraged patients' active participation in medical decision-making: cross-sectional survey (Preprint)

2018 ◽  
Author(s):  
Yen-Yuan Chen ◽  
Yu-Fang Cheng ◽  
Chau-Chung Wu ◽  
Tzong-Shinn Chu
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Health Information ◽  
Odds Ratio ◽  
Medical Decision Making ◽  
Active Role ◽  
Active Participation ◽  
Medical Decision ◽  
Confounding Variables ◽  
Artery Disease

BACKGROUND The rapid advance of information technology since the end of twentieth century has deeply influenced the ways people gather health information to use as references for medical decision-making. Studies have overwhelmingly been focused on the association between health information in the media and the usage of medical care, nevertheless, none of them have examined the association between the usage of social media for gathering health information and patients’ active participation in medical decision-making. OBJECTIVE The objective of this study was to examine the influence of the health information obtained from social media has on patients’ preference to actively participate in medical decision-making. METHODS The participants in this study were openly recruited from the patients who were admitted to the cardiology inpatient unit in the Department of Internal Medicine with an admission diagnosis of coronary artery disease. We used Control Preference Scale to estimate each patient’s preference to play an active role, a collaborative role, or a passive role in medical decision-making. We conducted multivariate logistic regression for examining the association between “gathering health information from social media” and “playing an active role in medical decision-making”, by including the confounding variables which have an association with the outcome variable with a p value of less than .30. RESULTS A total of 156 patients participated in this study. After adjusting for the confounding variables, patients who gathered health information related to coronary artery disease from social media were more likely to play an active role in medical decision-making (odds ratio = 2.85, p = 0.04). Furthermore, the odds of patients with one-year increment of their age for playing an active role in medical decision-making was decreased approximately by 6.20% (odds ratio = 0.94, p = 0.01), and patients cared for by Physician C preferred to play an active role as compared to other physicians (odds ratio = 5.37, p = 0.04). CONCLUSIONS Although health information gathered from social media may have been discussed and validated by one’s companions on social media, there is no guarantee that the health information is correct. If the health information gathered from social media is correct, patients’ active participation in medical decision-making is helpful in facilitating physician-patient communication towards that aim. Future studies may be focused on how information seekers use modern information technology to gather correct health information. CLINICALTRIAL N/A

scholarly journals Use of Tablets and Smartphones to Support Medical Decision Making in US Adults: Cross-Sectional Study (Preprint)

2020 ◽  
Author(s):  
Aisha Langford ◽  
Kerli Orellana ◽  
Jolaade Kalinowski ◽  
Carolyn Aird ◽  
Nancy Buderer
Keyword(s):  
Decision Making ◽  
Health Information ◽  
Medical Decision Making ◽  
Electronic Devices ◽  
Sociodemographic Factors ◽  
Medical Decision ◽  
Health And Wellness ◽  
Medical Decisions ◽  
Cross Sectional ◽  
Lower Odds

BACKGROUND Tablet and smartphone ownership have increased among US adults over the past decade. However, the degree to which people use mobile devices to help them make medical decisions remains unclear. OBJECTIVE The objective of this study is to explore factors associated with self-reported use of tablets or smartphones to support medical decision making in a nationally representative sample of US adults. METHODS Cross-sectional data from participants in the 2018 Health Information National Trends Survey (HINTS 5, Cycle 2) were evaluated. There were 3504 responses in the full HINTS 5 Cycle 2 data set; 2321 remained after eliminating respondents who did not have complete data for all the variables of interest. The primary outcome was use of a tablet or smartphone to help make a decision about how to treat an illness or condition. Sociodemographic factors including gender, race/ethnicity, and education were evaluated. Additionally, mobile health (mHealth)- and electronic health (eHealth)-related factors were evaluated including (1) the presence of health and wellness apps on a tablet or smartphone, (2) use of electronic devices other than tablets and smartphones to monitor health (eg, Fitbit, blood glucose monitor, and blood pressure monitor), and (3) whether people shared health information from an electronic monitoring device or smartphone with a health professional within the last 12 months. Descriptive and inferential statistics were conducted using SAS version 9.4. Weighted population estimates and standard errors, univariate odds ratios, and 95% CIs were calculated, comparing respondents who used tablets or smartphones to help make medical decisions (n=944) with those who did not (n=1377), separately for each factor. Factors of interest with a <i>P</i> value of &lt;.10 were included in a subsequent multivariable logistic regression model. RESULTS Compared with women, men had lower odds of reporting that a tablet or smartphone helped them make a medical decision. Respondents aged 75 and older also had lower odds of using a tablet or smartphone compared with younger respondents aged 18-34. By contrast, those who had health and wellness apps on tablets or smartphones, used other electronic devices to monitor health, and shared information from devices or smartphones with health care professionals had higher odds of reporting that tablets or smartphones helped them make a medical decision, compared with those who did not. CONCLUSIONS A limitation of this research is that information was not available regarding the specific health condition for which a tablet or smartphone helped people make a decision or the type of decision made (eg, surgery, medication changes). In US adults, mHealth and eHealth use, and also certain sociodemographic factors are associated with using tablets or smartphones to support medical decision making. Findings from this study may inform future mHealth and other digital health interventions designed to support medical decision making.

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