Use of comparative data for integrated cancer services

Wilkinson, Dawn; McCarthy, Mark

doi:10.1186/1472-6963-7-204

Cited by 7 publications

(20 citation statements)

References 13 publications

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“…The search yielded one instrument, a semi-structured telephone interview guide [133]. Questions focus on types of data used most often, for what purpose and to what capacity, and why data is not being used [133]. …”

Section: Resultsmentioning

confidence: 99%

Indicators and Measurement Tools for Health Systems Integration: A Knowledge Synthesis

Suter¹,

Oelke²,

Lima³

et al. 2017

Int J Integr Care

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Background:Despite far reaching support for integrated care, conceptualizing and measuring integrated care remains challenging. This knowledge synthesis aimed to identify indicator domains and tools to measure progress towards integrated care.Methods:We used an established framework and a Delphi survey with integration experts to identify relevant measurement domains. For each domain, we searched and reviewed the literature for relevant tools.Findings:From 7,133 abstracts, we retrieved 114 unique tools. We found many quality tools to measure care coordination, patient engagement and team effectiveness/performance. In contrast, there were few tools in the domains of performance measurement and information systems, alignment of organizational goals and resource allocation. The search yielded 12 tools that measure overall integration or three or more indicator domains.Discussion:Our findings highlight a continued gap in tools to measure foundational components that support integrated care. In the absence of such targeted tools, “overall integration” tools may be useful for a broad assessment of the overall state of a system.Conclusions:Continued progress towards integrated care depends on our ability to evaluate the success of strategies across different levels and context. This study has identified 114 tools that measure integrated care across 16 domains, supporting efforts towards a unified measurement framework.

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Section: Resultsmentioning

confidence: 99%

Indicators and Measurement Tools for Health Systems Integration: A Knowledge Synthesis

Suter¹,

Oelke²,

Lima³

et al. 2017

Int J Integr Care

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“…Five studies assessed data use and explored the factors influencing data use [26,27,52,70,71]. Four studies investigated the facilitators and barriers for achieving high quality data and systems [22,40,59,65].…”

Section: Resultsmentioning

confidence: 99%

“…Five studies were concerned with the assessment of data use and the factors influencing data use [26,27,52,70,71]. The other five included assessment of data use, but this was not always highlighted [40,45,50,61,62].…”

Section: Resultsmentioning

confidence: 99%

“…The remaining four studies combined qualitative and quantitative methods [26,45,61,71]. Interviews, questionnaire surveys, reviews of documentation and abstracts of relevant data were used in the studies.…”

Section: Resultsmentioning

confidence: 99%

“…Studies in China, the United Kingdom and Pakistan reported data users’ lack of trust in the quality of AIDS, cancer, and health management information systems due to unreliable or uncertain data [25,26,27]. …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Data Quality Assessment Methods for Public Health Information Systems

Chen

Hailey

Ning³

et al. 2014

IJERPH

253

205

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High quality data and effective data quality assessment are required for accurately evaluating the impact of public health interventions and measuring public health outcomes. Data, data use, and data collection process, as the three dimensions of data quality, all need to be assessed for overall data quality assessment. We reviewed current data quality assessment methods. The relevant study was identified in major databases and well-known institutional websites. We found the dimension of data was most frequently assessed. Completeness, accuracy, and timeliness were the three most-used attributes among a total of 49 attributes of data quality. The major quantitative assessment methods were descriptive surveys and data audits, whereas the common qualitative assessment methods were interview and documentation review. The limitations of the reviewed studies included inattentiveness to data use and data collection process, inconsistency in the definition of attributes of data quality, failure to address data users’ concerns and a lack of systematic procedures in data quality assessment. This review study is limited by the coverage of the databases and the breadth of public health information systems. Further research could develop consistent data quality definitions and attributes. More research efforts should be given to assess the quality of data use and the quality of data collection process.

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Networking in Disability for Development: Introducing the African Network for Evidence-to-Action on Disability (AfriNEAD)

Mji

Gcaza

Melling-Williams

et al. 2009

Disability &Amp; International Development

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Use of comparative data for integrated cancer services

Cited by 7 publications

References 13 publications

Indicators and Measurement Tools for Health Systems Integration: A Knowledge Synthesis

Indicators and Measurement Tools for Health Systems Integration: A Knowledge Synthesis

A Review of Data Quality Assessment Methods for Public Health Information Systems

Networking in Disability for Development: Introducing the African Network for Evidence-to-Action on Disability (AfriNEAD)

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