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Using Systems Engineering and Implementation Science to Design an Implementation Package for Preoperative Comprehensive Geriatric Assessment Among Older Adults Having Major Abdominal Surgery: Protocol for a 3-Phase Study

Using Systems Engineering and Implementation Science to Design an Implementation Package for Preoperative Comprehensive Geriatric Assessment Among Older Adults Having Major Abdominal Surgery: Protocol for a 3-Phase Study

All health care professionals working in the preoperative geriatric clinic (n=10) who are responsible for performing the tasks of the p CGA will be approached and consented for direct observation and focus groups to provide feedback on the p CGA process map. Additional staff (n=5) engaged in the clinic (administrative leads and schedulers) will be invited to participate.

Julia R Berian, Margaret L Schwarze, Nicole E Werner, Jane E Mahoney, Manish N Shah

JMIR Res Protoc 2024;13:e59428

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Academic Detailing as a Health Information Technology Implementation Method: Supporting the Design and Implementation of an Emergency Department–Based Clinical Decision Support Tool to Prevent Future Falls

Academic Detailing as a Health Information Technology Implementation Method: Supporting the Design and Implementation of an Emergency Department–Based Clinical Decision Support Tool to Prevent Future Falls

We conducted 16 semistructured academic detailing interviews with emergency medicine resident physicians (n=10) and advanced practice providers (n=6) who had previously encountered our CDS tool in practice, that is, within the last month. All interviews took place between August 2020 and June 2022, with 6 of the 16 interviews occurring prior to the implementation of the CDS hard stop (Figure 1). We purposively selected a range of participants based on how frequently they responded to the CDS.

Hanna J Barton, Apoorva Maru, Margaret A Leaf, Daniel J Hekman, Douglas A Wiegmann, Manish N Shah, Brian W Patterson

JMIR Hum Factors 2024;11:e52592

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Effectiveness of an Emergency Department–Based Machine Learning Clinical Decision Support Tool to Prevent Outpatient Falls Among Older Adults: Protocol for a Quasi-Experimental Study

Effectiveness of an Emergency Department–Based Machine Learning Clinical Decision Support Tool to Prevent Outpatient Falls Among Older Adults: Protocol for a Quasi-Experimental Study

This width provides sufficient precision for our study, considering that (1) a completion rate as small as 10% would be clinically meaningful and (2) we expect to observe a referral completion rate of 50% or higher—in which case, there would be nearly 100% power to reject a referral completion rate of 10% at a significance level of .05 when n=520.

Daniel J Hekman, Amy L Cochran, Apoorva P Maru, Hanna J Barton, Manish N Shah, Douglas Wiegmann, Maureen A Smith, Frank Liao, Brian W Patterson

JMIR Res Protoc 2023;12:e48128

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Evaluating the Usability of an Emergency Department After Visit Summary: Staged Heuristic Evaluation

Evaluating the Usability of an Emergency Department After Visit Summary: Staged Heuristic Evaluation

These experts include emergency medicine physicians (n=2), an ED nurse (n=1), a nurse with transitional care expertise (n=1), a primary care geriatrician (n=1), and an older adult care partner (n=1). The type of expertise each expert provided was unique. The care partner referred to their perspective as an older adult and their lived experience having previously visited the ED with their partner 14 times over the course of 10 weeks.

Hanna J Barton, Megan E Salwei, Rachel A Rutkowski, Kathryn Wust, Sheryl Krause, Peter LT Hoonakker, Paula vW Dail, Denise M Buckley, Alexis Eastman, Brad Ehlenfeldt, Brian W Patterson, Manish N Shah, Barbara J King, Nicole E Werner, Pascale Carayon

JMIR Hum Factors 2023;10:e43729

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