5 results on '"Fisher-Bowman, Jennifer"'
Search Results
2. Evaluation of a call center to assess post-discharge maternal and early neonatal outcomes of facility-based childbirth in Uttar Pradesh, India
- Author
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Gass, Jonathon D., Semrau, Katherine, Sana, Fatima, Mankar, Anup, Singh, Vinay Pratap, Fisher-Bowman, Jennifer, Neal, Brandon J., Tuller, Danielle E., Kumar, Bharath, Lipsitz, Stuart, Sharma, Narender, Kodkany, Bhala, Kumar, Vishwajeet, Gawande, Atul, and Hirschhorn, Lisa R.
- Subjects
Male ,Maternal Health ,Social Sciences ,lcsh:Medicine ,Surveys ,Labor and Delivery ,Sociology ,Medicine and Health Sciences ,lcsh:Science ,Call Centers ,Postpartum Period ,Obstetrics and Gynecology ,Quality Improvement ,Patient Discharge ,Professions ,Research Design ,Social Systems ,Engineering and Technology ,Female ,Research Article ,Postnatal Care ,Death Rates ,Equipment ,India ,Research and Analysis Methods ,Population Metrics ,Supervisors ,Humans ,Patient Reported Outcome Measures ,Spouses ,Communication Equipment ,Survey Research ,Population Biology ,lcsh:R ,Infant, Newborn ,Parturition ,Biology and Life Sciences ,Neonates ,Reproducibility of Results ,Health Surveys ,Health Care ,People and Places ,Birth ,Women's Health ,Population Groupings ,lcsh:Q ,Cell Phones ,Health Statistics ,Morbidity ,Developmental Biology ,Program Evaluation - Abstract
Background Maternal and neonatal outcomes in the immediate post-delivery period are critical indicators of quality of care. Data on childbirth outcomes in low-income settings usually require home visits, which can be constrained by cost and access. We report on the use of a call center to measure post-discharge outcomes within a multi-site improvement study of facility-based childbirth in Uttar Pradesh, India. Methods Of women delivering at study sites eligible for inclusion, 97.9% (n = 157,689) consented to follow-up. All consenting women delivering at study facilities were eligible to receive a phone call between days eight and 42 post-partum to obtain outcomes for the seven-day period after birth. Women unable to be contacted via phone were visited at home. Outcomes, including maternal and early neonatal mortality and maternal morbidity, were ascertained using a standardized script developed from validated survey questions. Data Quality Assurance (DQA) included accuracy (double coding of calls) and validity (consistency between two calls to the same household). Regression models were used to identify factors associated with inconsistency. Findings Over 23 months, outcomes were obtained by the call center for 98.0% (154,494/157,689) consenting women and their neonates. 87.9% of call center-obtained outcomes were captured by phone call alone and 12.1% required the assistance of a field worker. An additional 1.7% were obtained only by a field worker, 0.3% were lost-to-follow-up, and only 0.1% retracted consent. The call center captured outcomes with a median of 1 call (IQR 1โ2). DQA found 98.0% accuracy; data validation demonstrated 93.7% consistency between the first and second call. In a regression model, significant predictors of inconsistency included cases with adverse outcomes (p
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- 2018
3. Implementation and results of an integrated data quality assurance protocol in a randomized controlled trial in Uttar Pradesh, India
- Author
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Gass, Jonathon D., primary, Misra, Anamika, additional, Yadav, Mahendra Nath Singh, additional, Sana, Fatima, additional, Singh, Chetna, additional, Mankar, Anup, additional, Neal, Brandon J., additional, Fisher-Bowman, Jennifer, additional, Maisonneuve, Jenny, additional, Delaney, Megan Marx, additional, Kumar, Krishan, additional, Singh, Vinay Pratap, additional, Sharma, Narender, additional, Gawande, Atul, additional, Semrau, Katherine, additional, and Hirschhorn, Lisa R., additional
- Published
- 2017
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4. Evaluation of a call center to assess post-discharge maternal and early neonatal outcomes of facility-based childbirth in Uttar Pradesh, India.
- Author
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JrGass, Jonathon D., Semrau, Katherine, Sana, Fatima, Mankar, Anup, Singh, Vinay Pratap, Fisher-Bowman, Jennifer, Neal, Brandon J., Tuller, Danielle E., Kumar, Bharath, Lipsitz, Stuart, Sharma, Narender, Kodkany, Bhala, Kumar, Vishwajeet, Gawande, Atul, and Hirschhorn, Lisa R.
- Subjects
CALL centers ,CHILDBIRTH ,LOW-income countries ,QUALITY assurance - Abstract
Background: Maternal and neonatal outcomes in the immediate post-delivery period are critical indicators of quality of care. Data on childbirth outcomes in low-income settings usually require home visits, which can be constrained by cost and access. We report on the use of a call center to measure post-discharge outcomes within a multi-site improvement study of facility-based childbirth in Uttar Pradesh, India. Methods: Of women delivering at study sites eligible for inclusion, 97.9% (n = 157,689) consented to follow-up. All consenting women delivering at study facilities were eligible to receive a phone call between days eight and 42 post-partum to obtain outcomes for the seven-day period after birth. Women unable to be contacted via phone were visited at home. Outcomes, including maternal and early neonatal mortality and maternal morbidity, were ascertained using a standardized script developed from validated survey questions. Data Quality Assurance (DQA) included accuracy (double coding of calls) and validity (consistency between two calls to the same household). Regression models were used to identify factors associated with inconsistency. Findings: Over 23 months, outcomes were obtained by the call center for 98.0% (154,494/157,689) consenting women and their neonates. 87.9% of call center-obtained outcomes were captured by phone call alone and 12.1% required the assistance of a field worker. An additional 1.7% were obtained only by a field worker, 0.3% were lost-to-follow-up, and only 0.1% retracted consent. The call center captured outcomes with a median of 1 call (IQR 1โ2). DQA found 98.0% accuracy; data validation demonstrated 93.7% consistency between the first and second call. In a regression model, significant predictors of inconsistency included cases with adverse outcomes (p<0.001), and different respondents on the first and validation call (p<0.001). Conclusions: In areas with widespread mobile cell phone access and coverage, a call center is a viable and efficient approach for measurement of post-discharge childbirth outcomes. [ABSTRACT FROM AUTHOR]
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- 2018
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- View/download PDF
5. Implementation and results of an integrated data quality assurance protocol in a randomized controlled trial in Uttar Pradesh, India
- Author
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Gass, Jonathon D., Misra, Anamika, Yadav, Mahendra Nath Singh, Sana, Fatima, Singh, Chetna, Mankar, Anup, Neal, Brandon J., Fisher-Bowman, Jennifer, Maisonneuve, Jenny, Delaney, Megan Marx, Kumar, Krishan, Singh, Vinay Pratap, Sharma, Narender, Gawande, Atul, Semrau, Katherine, and Hirschhorn, Lisa R.
- Subjects
Data Quality Assurance (DQA) ,Safe Childbirth Checklist (SCC) ,Maternal morbidity ,Maternal and perinatal mortality ,Data feedback ,Supportive supervision ,Patient-reported outcomes ,Uttar Pradesh ,India ,Data accuracy ,Randomized control trial (RCT) - Abstract
Background: There are few published standards or methodological guidelines for integrating Data Quality Assurance (DQA) protocols into large-scale health systems research trials, especially in resource-limited settings. The BetterBirth Trial is a matched-pair, cluster-randomized controlled trial (RCT) of the BetterBirth Program, which seeks to improve quality of facility-based deliveries and reduce 7-day maternal and neonatal mortality and maternal morbidity in Uttar Pradesh, India. In the trial, over 6300 deliveries were observed and over 153,000 mother-baby pairs across 120 study sites were followed to assess health outcomes. We designed and implemented a robust and integrated DQA system to sustain high-quality data throughout the trial. Methods: We designed the Data Quality Monitoring and Improvement System (DQMIS) to reinforce six dimensions of data quality: accuracy, reliability, timeliness, completeness, precision, and integrity. The DQMIS was comprised of five functional components: 1) a monitoring and evaluation team to support the system; 2) a DQA protocol, including data collection audits and targets, rapid data feedback, and supportive supervision; 3) training; 4) standard operating procedures for data collection; and 5) an electronic data collection and reporting system. Routine audits by supervisors included double data entry, simultaneous delivery observations, and review of recorded calls to patients. Data feedback reports identified errors automatically, facilitating supportive supervision through a continuous quality improvement model. Results: The five functional components of the DQMIS successfully reinforced data reliability, timeliness, completeness, precision, and integrity. The DQMIS also resulted in 98.33% accuracy across all data collection activities in the trial. All data collection activities demonstrated improvement in accuracy throughout implementation. Data collectors demonstrated a statistically significant (p = 0.0004) increase in accuracy throughout consecutive audits. The DQMIS was successful, despite an increase from 20 to 130 data collectors. Conclusions: In the absence of widely disseminated data quality methods and standards for large RCT interventions in limited-resource settings, we developed an integrated DQA system, combining auditing, rapid data feedback, and supportive supervision, which ensured high-quality data and could serve as a model for future health systems research trials. Future efforts should focus on standardization of DQA processes for health systems research. Trial Registration ClinicalTrials.gov identifier, NCT02148952. Registered on 13 February 2014.
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- 2017
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