Experiences of Women With Disabilities in Accessing and Receiving Contraceptive Care

Background Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of a person on the basis of irrelevant characteristics such as race or gender. This review examines the evidence that healthcare professionals display implicit biases towards patients. Methods PubMed, PsychINFO, PsychARTICLE and CINAHL were searched for peer-reviewed articles published between 1st March 2003 and 31st March 2013. Two reviewers assessed the eligibility of the identified papers based on precise content and quality criteria. The references of eligible papers were examined to identify further eligible studies. Results Forty two articles were identified as eligible. Seventeen used an implicit measure (Implicit Association Test in fifteen and subliminal priming in two), to test the biases of healthcare professionals. Twenty five articles employed a between-subjects design, using vignettes to examine the influence of patient characteristics on healthcare professionals’ attitudes, diagnoses, and treatment decisions. The second method was included although it does not isolate implicit attitudes because it is recognised by psychologists who specialise in implicit cognition as a way of detecting the possible presence of implicit bias. Twenty seven studies examined racial/ethnic biases; ten other biases were investigated, including gender, age and weight. Thirty five articles found evidence of implicit bias in healthcare professionals; all the studies that investigated correlations found a significant positive relationship between level of implicit bias and lower quality of care. Discussion The evidence indicates that healthcare professionals exhibit the same levels of implicit bias as the wider population. The interactions between multiple patient characteristics and between healthcare professional and patient characteristics reveal the complexity of the phenomenon of implicit bias and its influence on clinician-patient interaction. The most convincing studies from our review are those that combine the IAT and a method measuring the quality of treatment in the actual world. Correlational evidence indicates that biases are likely to influence diagnosis and treatment decisions and levels of care in some circumstances and need to be further investigated. Our review also indicates that there may sometimes be a gap between the norm of impartiality and the extent to which it is embraced by healthcare professionals for some of the tested characteristics. Conclusions Our findings highlight the need for the healthcare profession to address the role of implicit biases in disparities in healthcare. More research in actual care settings and a greater homogeneity in methods employed to test implicit biases in healthcare is needed.

Persons with disabilities face greater barriers to health care than do those without disabilities ( 1 ). To identify characteristics of noninstitutionalized adults with six specific disability types (hearing, vision, cognition, mobility, self-care, and independent living),* and to assess disability-specific disparities in health care access, CDC analyzed 2016 Behavioral Risk Factor Surveillance System (BRFSS) data. The prevalences of disability overall and by disability type, and access to health care by disability type, were estimated. Analyses were stratified by three age groups: 18–44 years (young adults), 45–64 years (middle-aged adults), and ≥65 years (older adults). Among young adults, cognitive disability (10.6%) was the most prevalent type. Mobility disability was most prevalent among middle-aged (18.1%) and older adults (26.9%). Generally, disability prevalences were higher among women, American Indians/Alaska Natives (AI/AN), adults with income below the federal poverty level (FPL), and persons in the South U.S. Census region. Disability-specific disparities in health care access were prevalent, particularly among young and middle-aged adults. These data might inform public health programs of the sociodemographic characteristics and disparities in health care access associated with age and specific disability types and guide efforts to improve access to care for persons with disabilities.

Introduction Applying Design Thinking to health care could enhance innovation, efficiency, and effectiveness by increasing focus on patient and provider needs. The objective of this review is to determine how Design Thinking has been used in health care and whether it is effective. Methods We searched online databases (PubMed, Medline, Web of Science, CINAHL, and PyscINFO) for articles published through March 31, 2017, using the terms “health,” “health care,” or “healthcare”; and “Design Thinking,” “design science,” “design approach,” “user centered design,” or “human centered design.” Studies were included if they were written in English, were published in a peer-reviewed journal, provided outcome data on a health-related intervention, and used Design Thinking in intervention development, implementation, or both. Data were collected on target users, health conditions, intervention, Design Thinking approach, study design or sample, and health outcomes. Studies were categorized as being successful (all outcomes improved), having mixed success (at least one outcome improved), or being not successful (no outcomes improved). Results Twenty-four studies using Design Thinking were included across 19 physical health conditions, 2 mental health conditions, and 3 systems processes. Twelve were successful, 11 reported mixed success, and one was not successful. All 4 studies comparing Design Thinking interventions to traditional interventions showed greater satisfaction, usability, and effectiveness. Conclusion Design Thinking is being used in varied health care settings and conditions, although application varies. Design Thinking may result in usable, acceptable, and effective interventions, although there are methodological and quality limitations. More research is needed, including studies to isolate critical components of Design Thinking and compare Design Thinking–based interventions with traditionally developed interventions.