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Updated: Jan 22, 2026

Surgical Techniques to Optimize Ovarian Reserve during Laparoscopic Cystectomy for Ovarian Endometrioma
Published on: January 22, 2022
Using qualitative research to reduce readmissions and optimize perioperative cystectomy care
Angela B Smith1, Dana Mueller1, Brandon Garren1
1Department of Urology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Background:
Preventable complications and readmissions after cystectomy may be detectable via postoperative monitoring of patient-reported outcomes (PROs). However, no study has defined meaningful PROs or the use of mobile communication devices (mobile health [mHealth]) to capture them. The objectives of this study were to determine which high-priority PROs influence patients' perioperative experience, what processes influence these outcomes, how patients and caregivers differ in their experiences, and how mHealth might be used to improve outcomes.
Methods:
Forty-five semistructured, in-depth interviews were conducted with readmitted cystectomy patients, caregivers, and providers with an interview guide that addressed perioperative education, symptoms, function, and the potential for mHealth interventions. Among 15 patients, 10 had an interviewed partner. A thematic analysis of interviews conducted with readmitted patients, caregivers, and providers was performed to examine processes that affected perioperative care and readmission and to determine how mHealth interventions might be implemented.
Results:
Readmitted patients and caregivers ranged in age from 33 to 78 years and were diverse in race and stage. The providers included a diverse representation of physicians, nurses, and other specialists. Cystectomy preoperative education was overwhelming and lacked personalization, and this contributed to a fundamental lack of knowledge regarding normal and abnormal symptoms after surgery. Three connecting themes were identified: 1) cystectomy education overload, 2) a need to define normal symptoms, and 3) education with incremental learning through mHealth.
Conclusions:
A personalized mHealth intervention addressing themes of education overload, the definition of normality, and incremental learning could be realized through mHealth technology and provide the right information for the right patient at the right time.
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