Perceptions and experiences of multiple sclerosis patients regarding out-of-pocket costs of care discussions

Factors Associated with Willingness to Pay for Cost-Sharing under Universal Health Coverage Scheme in Yogyakarta, Indonesia: A Cross-Sectional Survey

BACKGROUND

National Health Insurance (NHI) in Indonesia requires an appropriate cost-sharing policy, particularly for diseases that require the largest financing. This study examined factors that influence willingness to pay (WTP) for cost-sharing under the universal health coverage scheme among patients with catastrophic illnesses in Yogyakarta, Indonesia.

METHODS

This was a cross-sectional study using structured questionnaires through direct interviews. The factors related to the WTP for cost-sharing under the NHI scheme in Indonesia were identified by a bivariable logistic regression analysis.

RESULTS

Two out of every five (41.2%) participants had willingness to pay for cost-sharing. Sex [AOR = 0.69 (0.51, 0.92)], education [AOR = 1.54 (0.67, 3.55)], family size [AOR = 1.71 (1.07, 2.73)], occupation [AOR = 1.35 (0.88, 2.07)], individual income [AOR = 1.50 (0.87, 2.61)], household income [AOR = 1.47 (0.90, 2.39)], place of treatment [AOR = 2.54 (1.44, 4.45)], a health insurance plan [AOR = 1.22 (0.87, 1.71)], and whether someone receives an inpatient or outpatient service [AOR = 0.23 (0.10, 0.51)] were found to affect the WTP for a cost-sharing scheme with p < 0.05.

CONCLUSION

Healthcare (place of treatment, health insurance plan, and whether someone receives an inpatient or outpatient service) and individual socioeconomic (sex, educational, family size, occupational, income) factors were significantly related to the WTP for cost-sharing.

Costs Versus Quality in Imaging Examination Decisions

PURPOSE

Emerging price transparency tools allow consumers to access individualized out-of-pocket cost (OOPC) estimates, but many lack quality metrics. The aim of this study was to evaluate how potential patients weigh imaging OOPC versus measures of quality when selecting an imaging center for a hypothetical health condition (back pain).

METHODS

Surveying 1,310 Amazon Mechanical Turk volunteers, the authors evaluated how potential patients weigh MRI OOPC ($50 vs $400 vs unknown cost at the time of the examination, with billed OOPC responsibility varying between $50 and $3,500) versus service quality surrogates using three different quality indicators (examination results accuracy, physician recommendation of an imaging center on the basis of familiarity, and facility online star ratings) in their decisions when selecting a radiology center for imaging of two hypothetical clinical conditions (mild and severe back pain), using ranking-based conjoint analyses.

RESULTS

A total of 1,025 eligible respondents completed the survey. Respondents expressed higher preference for perceived quality over cost in hypothetical severe back pain scenarios, resulting in a relative importance of 65.8% (95% confidence interval [CI], 62.2%-69.4%) for improved imaging results accuracy from 87% to 96%, 63.9% (95% CI, 60.3%-67.5%) for provider recommendations of the facility, and 80.1% (95% CI, 74.2%-85.9%) for an increase in online review star ratings from 2.5 to 4.5 (out of 5) compared with an increased cost from $50 to $400. For mild back pain, there was no statistical difference in respondents' preference for perceived quality and cost.

CONCLUSIONS

Incorporating quality metrics into price transparency tools is important. Further research is needed to identify metrics that are most comparable and easily obtainable across imaging centers that remain important to patients.

What People Tweet about Imaging Costs

BACKGROUND

Patients may experience adverse health outcomes when they are unable to manage medical bills. It is currently unknown, however, whether patients talk about cost in the context of medical imaging services they received.

MATERIALS AND METHODS

Retrospective qualitative analysis of twitter posts related to medical imaging and cost. Tweets were extracted from twitter, inclusion and exclusion criteria were applied, and tweets were categorized as either "positive" or "negative"; none were "neutral". A qualitative thematic analysis of all included tweets was performed to develop themes and topics expressed. A single tweet may have been assigned several different codes according to its content. A random sampling of the tweets from each topic were selected by the two reviewers, verified by the remaining reviewers, and quoted (Q).

RESULTS

Here, 9.8% (n = 99) of tweets relevant to medical imaging were included in the analysis. The majority had a negative sentiment (91%, n = 90) related to themes of (1) cost of care (47%, n = 42), (2) care delivery (23%, n = 21), (3) insurance Issues (23%, n = 21), and (4) need for information (7%, n = 6). A few positive tweets (9%, n = 9) were related to themes of (1) Gratitude (44%, n = 4), (2) Affordability (33%, n = 3), and (3) Better than expected (22%, n = 2).

CONCLUSION

Among tweets related to medical imaging we found that 10% relate to cost and that these are overwhelmingly negative, mostly due to perceived high cost of care, deficient care delivery, and insurance issues.