Video Intervention and Goals-of-Care Documentation in Hospitalized Older Adults

Volandes, Angelo E.; Zupanc, Sophia N.; Lakin, Joshua R.; Cabral, Howard J.; Burns, Edith A.; Carney, Maria T.; Lopez, Santiago; Itty, Jennifer; Emmert, Kaitlin; Martin, Narda J.; Cole, Therese; Dobie, Alexandra; Cucinotta, Traci; Joel, Milton; Caruso, Lisa B.; Henault, Lori; Dugas, Julianne N.; Astone, Kristina; Winter, Michael; Wang, Na; Davis, Aretha Delight; Garde, Cynthia; Rodriguez, Perla Macip; El-Jawahri, Areej; Moseley, Edward T.; Das, Sophiya; Sciacca, Kate; Ramirez, Ana Maria; Gromova, Valeria; Lambert, Sherene; Sanghani, Shreya; Lindvall, Charlotta; Paasche-Orlow, Michael K.

doi:10.1001/jamanetworkopen.2023.32556

Cited by 11 publications

(3 citation statements)

References 61 publications

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“…Across the 3 patient cohorts, we observed low-value care in 7627 of the 37 116 control patient-months (20.5%) and 7416 of the 46 381 intervention patient-months (16.0%) (Table 2 and Figure 3). Overall, 554 patients in the diabetes cohort (20.7%), 1362 patients in the insomnia/anxiety cohort (32.2%), and 143 patients in the prostate cancer screening cohort (6.1%) received low-value care.…”

Section: Resultsmentioning

confidence: 98%

Using Behavioral Economics to Reduce Low-Value Care Among Older Adults

Kullgren,

Kim,

Slowey

et al. 2024

JAMA Intern Med

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ImportanceUse of low-value care is common among older adults. It is unclear how to best engage clinicians and older patients to decrease use of low-value services.ObjectiveTo test whether the Committing to Choose Wisely behavioral economic intervention could engage primary care clinicians and older patients to reduce low-value care.Design, Setting, and ParticipantsStepped-wedge cluster randomized clinical trial conducted at 8 primary care clinics of an academic health system and a private group practice between December 12, 2017, and September 4, 2019. Participants were primary care clinicians and older adult patients who had diabetes, insomnia, or anxiety or were eligible for prostate cancer screening. Data analysis was performed from October 2019 to November 2023.InterventionClinicians were invited to commit in writing to Choosing Wisely recommendations for older patients to avoid use of hypoglycemic medications to achieve tight glycemic control, sedative-hypnotic medications for insomnia or anxiety, and prostate-specific antigen tests to screen for prostate cancer. Committed clinicians had their photographs displayed on clinic posters and received weekly emails with alternatives to these low-value services. Educational handouts were mailed to applicable patients before scheduled visits and available at the point of care.Main Outcomes and MeasuresPatient-months with a low-value service across conditions (primary outcome) and separately for each condition (secondary outcomes). For patients with diabetes, or insomnia or anxiety, secondary outcomes were patient-months in which targeted medications were decreased or stopped (ie, deintensified).ResultsThe study included 81 primary care clinicians and 8030 older adult patients (mean [SD] age, 75.1 [7.2] years; 4076 men [50.8%] and 3954 women [49.2%]). Across conditions, a low-value service was used in 7627 of the 37 116 control patient-months (20.5%) and 7416 of the 46 381 intervention patient-months (16.0%) (adjusted odds ratio, 0.79; 95% CI, 0.65-0.97). For each individual condition, there were no significant differences between the control and intervention periods in the odds of patient-months with a low-value service. The intervention increased the odds of deintensification of hypoglycemic medications for diabetes (adjusted odds ratio, 1.85; 95% CI, 1.06-3.24) but not sedative-hypnotic medications for insomnia or anxiety.Conclusions and RelevanceIn this stepped-wedge cluster randomized clinical trial, the Committing to Choose Wisely behavioral economic intervention reduced low-value care across 3 common clinical situations and increased deintensification of hypoglycemic medications for diabetes. Use of scalable interventions that nudge patients and clinicians to achieve greater value while preserving autonomy in decision-making should be explored more broadly.Trial RegistrationClinicalTrials.gov Identifier: NCT03411525

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Section: Resultsmentioning

confidence: 98%

Using Behavioral Economics to Reduce Low-Value Care Among Older Adults

Kullgren,

Kim,

Slowey

et al. 2024

JAMA Intern Med

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“…A study of video intervention and documentation of goals of care found that documentation was greater in the intervention group than in the control group [ 15 ]. The 2x2 contingency table was (3744, 2279, 2396, 2383), with a p-value of < 0.001 by Fisher’s exact.…”

Section: Resultsmentioning

confidence: 99%

“…A study of minimally invasive surfactant therapy (MIST) in preterm infants found that hospitalization in the first two years was 25.1% in the MIST group and 38.2% in the control group [14]. A study of video intervention and documentation of goals of care found that documentation was greater in the intervention group than in the control group [15]. The 2x2 contingency table was (3744, 2279, 2396, 2383), with a p-value of < 0.001 by Fisher's exact.…”

Section: Case Studiesmentioning

confidence: 99%

Statistical Significance Versus Clinical Relevance: A Head-to-Head Comparison of the Fragility Index and Relative Risk Index

Heston

2023

Cureus

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Background In biostatistics, assessing the fragility of research findings is crucial for understanding their clinical significance. This study focuses on the fragility index, unit fragility index, and relative risk index as measures to evaluate statistical fragility. The fragility indices assess the susceptibility of p-values to change significance with minor alterations in outcomes within a 2x2 contingency table. In contrast, the relative risk index quantifies the deviation of observed findings from therapeutic equivalence, the point at which the relative risk equals 1. While the fragility indices have intuitive appeal and have been widely applied, their behavior across a wide range of contingency tables has not been rigorously evaluated. Methods Using a Python software program, a simulation approach was employed to generate random 2x2 contingency tables. All tables under consideration exhibited p-values < 0.05 according to Fisher's exact test. Subsequently, the fragility indices and the relative risk index were calculated. To account for sample size variations, the indices were divided by the sample size to give fragility and risk quotients. A correlation matrix assessed the collinearity between each metric and the p-value. Results The analysis included 2,000 contingency tables with cell counts ranging from 20 to 480. Notably, the formulas for calculating the fragility indices encountered limitations when cell counts approached zero or duplicate cell counts hindered standardized application. The correlation coefficients with p-values were as follows: unit fragility index (-0.806), fragility index (-0.802), fragility quotient (-0.715), unit fragility quotient (-0.695), relative risk index (-0.403), and risk quotient (-0.261). Conclusion The fragility indices and fragility quotients demonstrated a strong correlation with p-values below 0.05, while the relative risk index and relative risk quotient exhibited a weak association with p-values below this threshold. This implies that the fragility indices offer limited additional information beyond the p-value alone. In contrast, the relative risk index and risk quotient exhibit independence from the p-value, indicating that they may provide important additional information about statistical fragility by evaluating the divergence of observed results from therapeutic equivalence, irrespective of the p-value-based statistical significance.

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