scholarly journals M-Health Intervention Has the Potential to Alleviate Constraints in Using Administrative Data Systems for Improving Service Delivery in India

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 802-802
Author(s):  
Rasmi Avula ◽  
Purnima Menon ◽  
Sumati Bajaj ◽  
Malvika Saxena ◽  
Shilpa Deshpande ◽  
...  
Keyword(s):  
Child Development ◽  
Administrative Data ◽  
Data Use ◽  
Data Entry ◽  
Program Improvement ◽  
Data Systems ◽  
Data Completeness ◽  
Local Program ◽  
Supervisory Staff ◽  
Monitor Data

Abstract Objectives Administrative Data (AD) are a key component of nutrition data systems, but little is known about how AD are used or can be reshaped for accountability and action. We studied factors influencing AD use in India's Integrated Child Development Services (ICDS), in two Indian states; and assessed how an m-Health intervention (Common Application Software (CAS)) changes it. ICDS-AD system consolidates data from 11-recording registers updated every month by ∼1.3 million frontline workers (FLW). CAS digitizes these records and creates dashboards for real-time monitoring. Methods We conducted semi-structured interviews (n = 82) with supervisory staff at sub-district [Lady Supervisors (LS); Child Development Project Officer (CDPO)] and district level [District Program Officer (DPO)] to understand AD use patterns, facilitators and barriers. Data were coded, summarized and analysed across levels of supervisory staff. Results LS supervise FLWs, visit villages monthly to monitor data, and collate it to the sub-district level. Due to limited time and pressures to update AD on time, LS focus on data completeness and not on quality or data use. At the next level, CDPOs review and discuss program targets for indicators with LS, and check data completeness. They create monthly reports on targets for the districts, based on LS reports. At the district, DPOs use compiled reports to review subdistrict performance and discuss any gaps with CDPOs. CAS makes the data entry, compilation and report generation easier for all users. LS can monitor data entry daily in CAS and data collation is automated. CDPOs can review the CAS dashboard and use it to discuss issues with LS and DPOs. However, program target indicators dominate use of data. In CAS areas, these include growth monitoring completion, home visits, identification of malnourished children, and their referral. Overall, data use is target-driven, with indicators for monitoring prioritized by program management. Conclusions Focus of data use has been on reporting up on target indicators rather than on local program improvement, due to staff and time constraints. CAS can address some AD use constraints by easing data entry and data compilation but a data culture that focuses on predetermined target indicators can limit use for local program improvement. Funding Sources Bill & Melinda Gates Foundation via grants to IFPRI and UCSF.

scholarly journals Supporting A Developmental Approach for Shared Data Infrastructure

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Amy Hawn Nelson ◽  
Emily Berkowitz
Keyword(s):  
Data Integration ◽  
Learning Community ◽  
Administrative Data ◽  
Technical Assistance ◽  
Developmental Process ◽  
Program Improvement ◽  
Data Systems ◽  
Developmental Approach ◽  
Data Infrastructure ◽  
Independent Evaluation

BackgroundPublic agencies hold important, yet largely unused, administrative data on the families and communities they serve. Integrated Data Systems (IDS) provide the governance process, legal framework, technology, and human capacity to connect these families and communities across data siloes. By securely linking administrative data across siloes, IDS are able to support data-informed decision making. IntroductionFor 10+ years, AISP has helped jurisdictions through the developmental process of building IDS. We operate a network of 22 U.S. states and counties with fully-functioning Integrated Data Systems, and provide technical assistance to 18 jurisdictions at various stages of IDS development. Objectives and ApproachThis session presents the outcomes of an independent evaluation of our Learning Community initiative (2019) and presents a new developmental framework that outlines key dimensions of quality and readiness for IDS building and implementation. ResultsAs of 2020, 20 sites have received formal 18-month cohort based technical assistance. This presentation will discuss site-based approaches to facilitate data sharing, including common challenges and solutions, and progress to date, including findings of an independent evaluation (2019). We will also present a framework developed based on the deep knowledge developed through technical assistance efforts, and findings from a national survey of data integration efforts conducted in 2020. The framework uses purpose, partnership structure, technical architecture, and organizational model—with respect to the strengths and challenges of each—to categorize and synthesize data integration efforts for social policy and program improvement. The developmental approach to our work emphasizes that we seek to understand methods for sustainability in diverse ways. Conclusion / ImplicationsWhile there is broad agreement in the value of integrating data across domains, developing the capacity and skills necessary to link administrative data for policy evaluation and research remains an elusive goal. Initial results indicate that an individualized yet collaborative technical assistance approach is successful in developing data integration capacity.

scholarly journals Multi-province epidemiological research using linked administrative data: a case study from Canada.

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Amanda Leanne Butler ◽  
Mark Smith ◽  
Wayne Jones ◽  
Carol E Adair ◽  
Simone Vigod ◽  
...  
Keyword(s):  
Information Systems ◽  
Administrative Data ◽  
Research Process ◽  
Health Data ◽  
Provincial Government ◽  
Data Systems ◽  
Administrative Health Data ◽  
Healthcare Data ◽  
Research Activities

BackgroundCanada has a publicly-funded universal healthcare system with information systems managed by 13 different provinces and territories. This context creates inconsistencies in data collection and challenges for research or surveillance conducted at the national or multi-jurisdictional level. ObjectiveUsing a recent Canadian research project as a case study, we document the strengths and challenges of using administrative health data in a multi-jurisdictional context. We discuss the implications of using different health information systems and the solutions we adopted to deal with variations. Our goal is to contribute to better understanding of these challenges and the development of a more integrated and harmonized approach to conducting multi-jurisdictional research using administrative data. Context and ModelUsing data from five separate provincial healthcare data systems, we sought to create and report on a set of provincially-comparable mental health and addiction services performance indicators. In this paper, we document the research process, challenges, and solutions. Finally, we conclude by making recommendations for investment in national infrastructure that could help cut costs, broaden scope, and increase use of administrative health data that exists in Canada. ConclusionCanada has an incredible wealth of administrative data that resides in 13 territorial and provincial government systems. Navigating access and improving comparability across these systems has been an ongoing challenge for the past 20 years, but progress is being made. We believe that with some investment, a more harmonized and integrated information network could be developed that supports a broad range of surveillance and research activities with strong policy and program implications.

Handbook on Using Administrative Data for Research and Evidence-based Policy

2020 ◽  
Keyword(s):  
Administrative Data ◽  
Data Security ◽  
Data Use ◽  
Decision Makers ◽  
Institutional Review Boards ◽  
Evidence Based ◽  
Evidence Based Policy ◽  
Institutional Review ◽  
Address Data ◽  
The Us

The Handbook on Using Administrative Data for Research and Evidence-based Policy offers guidance for researchers, data providers, and decision-makers who would like to use administrative data to inform policy. Administrative data has the potential to change the future of research, in particular when combined with experiments that can help test the effectiveness of planned programs and evaluate new hypotheses. This Handbook offers a roadmap to overcome potential challenges in using administrative data for research and evaluation purposes. The technical chapters address data use agreements, working with institutional review boards, physical data security, privacy, and more. Ten complementary case studies showcase diverse models of successful administrative data partnerships in the US, Canada, Europe, Africa, and Asia.

Revitalizing ICDS

2019 ◽  
pp. 1409-1427
Author(s):  
Jeremiah Jacob
Keyword(s):  
Child Development ◽  
Young Children ◽  
Nutritional Status ◽  
Mortality Rates ◽  
Government Program ◽  
The World ◽  
The Government ◽  
Supervisory Staff ◽  
Vital Parameters

ICDS-Integrated Child Development Services is India's only government program for combating the rampant malnutrition prevalent in young children. In this chapter, the authors aim to examine the need and scope of ICDS scheme, its services and countrywide reach; considering that every fifth child in the world lives in India, this scheme is critical to ensuring that today's children who are our citizens of tomorrow are well nurtured and nourished, thus securing the country's future. Also its efficacy in achieving stated objectives is assessed through analysis of vital parameters such as nutritional status, mortality rates etc. Further, the bottlenecks facing the scheme such as lack of adequate sanitation facilities and supervisory staff etc. are studied and the initiatives taken by the government to revitalize it are also examined. The transformation into Mission Mode has ushered in programmatic, institutional and management reforms and renewed thrust on creating awareness through an Information, Education and Communication (IEC) campaign.

Using Qualitative Inquiry and Self-Study to Make Sense of Data Use Practices in Teacher Education

2020 ◽  
Vol 11 (4) ◽  
pp. 34-44
Author(s):  
Jahnette Wilson ◽  
Sam Brower ◽  
Teresa Edgar ◽  
Amber Thompson ◽  
Shea Culpepper
Keyword(s):  
Teacher Education ◽  
Teacher Preparation ◽  
Qualitative Data ◽  
Data Use ◽  
Program Improvement ◽  
Qualitative Inquiry ◽  
Teacher Preparation Program ◽  
Preparation Program ◽  
Self Study

Accountability and rigor in teacher education have been the focus of recent policy initiatives. Thus, data use practices have become increasingly critical to informing program improvement. Educational researchers have established self-study as a research methodology to intentionally be used by teacher educators to improve their practice. The purpose of the self-study described in this article was to examine the data use practices of one teacher preparation program in an effort to facilitate improvement of the program's capacity in using program data. The qualitative data gathered in this case study proved to be pivotal in the continuous improvement efforts of the teacher preparation program; thus, the usefulness and value of the findings within this case study have implications for how institutional self-study and qualitative data can support quantitative programmatic data in order to facilitate programmatic improvement initiatives.

Are Structured Data Structured Identically?

2005 ◽  
Vol 44 (05) ◽  
pp. 631-638 ◽  
Author(s):  
J. Roukema ◽  
A. M. van Ginneken ◽  
M. de Wilde ◽  
J. van der Lei ◽  
R. K. Los
Keyword(s):  
Clinical Practice ◽  
Data Use ◽  
Data Entry ◽  
Patient Data ◽  
Structured Data ◽  
Free Text ◽  
Patient Records ◽  
Paper Record ◽  
Recorded Data ◽  
Insight Into

Summary Objective: OpenSDE is an application that supports structured recording of narrative patient data to enable use of the data in both clinical practice and clinical research. Reliability and accuracy of collected data are essential for subsequent data use. In this study we analyze the uniformity of data entered with OpenSDE. Our objective is to obtain insight into the consensus and differences of recorded data. Methods: Three pediatricians transcribed 20 paper patient records using OpenSDE. The transcribed records were compared and all recorded findings were classified into one of six categories of difference. Results: Of all findings 22% were recorded identically; 17% of the findings were recorded differently (predominantly as free text); 61% was omitted, inferred, or in conflict with the paper record. Conclusion: The results of this study show that recording patient data using structured data entry does not necessarily lead to uniformly structured data.

Case Study and Self Study as Means for Program Improvement in Teacher Education

2021 ◽  
pp. 225-241
Author(s):  
Jahnette Wilson ◽  
Samuel R. Brower ◽  
Teresa Edgar ◽  
Amber Thompson ◽  
Shea Culpepper
Keyword(s):  
Teacher Education ◽  
Qualitative Data ◽  
Data Use ◽  
Program Improvement ◽  
Preparation Program ◽  
Policy And Practice ◽  
Educator Preparation ◽  
Educator Preparation Program ◽  
K 12

Proponents of the evidence-based movement in education maintain that decisions around policy and practice should be grounded in data outcomes. However, insufficient research exists on data use in teacher education programs as much of the research on data use is concentrated on K-12 programs. The purpose of this case study was to investigate the data use practices of an educator preparation program so as to facilitate program improvement efforts. The collective qualitative data described in this study was key to informing continuous improvement areas within this educator preparation program. Therefore, this case study offers insight as to how qualitative data can support and inform program improvement efforts.

Trial prospector: An automated clinical trials eligibility matching program.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 6538-6538 ◽  
Author(s):  
Andrew J. Parchman ◽  
Guo-Qiang Zhang ◽  
Patrick Mergler ◽  
Jill Barnholtz-Sloan ◽  
Robert Lanese ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Language Processing ◽  
Clinical Data ◽  
Point Of Care ◽  
Effective Means ◽  
Data Entry ◽  
Cancer Center ◽  
Data Systems ◽  
Eligibility Criteria ◽  
Pathologic Diagnosis

6538 Background: Clinical trials are the evidence base for improving cancer treatment. Unfortunately, only approximately 5% of cancer patients (pts) take part in clinical research studies. Even in settings where clinical trials (CTs) are available, pt participation remains low. We hypothesize that the time required to identify appropriate studies for individual pts is a significant barrier to clinical trial accrual. Methods: Our multidisciplinary team developed Trial Prospector (TP), an innovative and flexible computer-based system that utilizes artificial intelligence and natural language processing to automatically extract information (e.g. demographics, pathologic diagnosis, stage, labs) from multiple clinical data systems and then match it to CT eligibility criteria. A user-friendly interface allows physician perusal of relevant CTs and eligibility checklists at the point of care without requiring manual data entry. We pilot tested TP in our cancer center GI oncology subspecialty clinics. TP was deployed for consecutive new pts, and oncologists (oncs) completed surveys after each visit to assess the usability and impact of TP. Results: Eleven medical oncologists (6 attendings and 5 fellows) participated in the pilot study. TP was deployed during 60 new pt visits. Of the 15 relevant GI/phase I CTs, TP identified a mean of 7 ± 2.7 eligible trials per patient. The most common reasons for ineligibility were pathologic diagnosis and labs. CTs were considered by the treating onc for 66.7% of the pts. 95% of participating oncs reviewed the TP output at the point of care with 70% spending 0-5 minutes assessing eligibility. Of the pts considered for CTs, a TP report was reviewed 72.5% of the time. Oncs reported that TP saved time identifying potential CTs during 57.1% of the visits. The reports were manually reviewed, and the TP matching algorithm was 100% accurate. 90.9% of the oncs recommended TP for CT screening. Conclusions: These results indicate that Trial Prospector is a feasible, accurate, and effective means to identify CTs for individual pts during a busy outpatient oncology clinic. Ongoing refinements will expand clinical data extraction and CT warehouse to improve precision and applicability across diseases.

Sign in / Sign up

Export Citation Format

Share Document