Dataset Information

Toward Timely Data for Cancer Research: Assessment and Reengineering of the Cancer Reporting Process.

ABSTRACT: BACKGROUND:Cancer registries systematically collect cancer-related data to support cancer surveillance activities. However, cancer data are often unavailable for months to years after diagnosis, limiting its utility. OBJECTIVE:The objective of this study was to identify the barriers to rapid cancer reporting and identify ways to shorten the turnaround time. METHODS:Certified cancer registrars reporting to the Indiana State Department of Health cancer registry participated in a semistructured interview. Registrars were asked to describe the reporting process, estimate the duration of each step, and identify any barriers that may impact the reporting speed. Qualitative data analysis was performed with the intent of generating recommendations for workflow redesign. The existing and redesigned workflows were simulated for comparison. RESULTS:Barriers to rapid reporting included access to medical records from multiple facilities and the waiting period from diagnosis to treatment. The redesigned workflow focused on facilitating data sharing between registrars and applying a more efficient queuing technique while registrars await the delivery of treatment. The simulation results demonstrated that our recommendations to reduce the waiting period and share information could potentially improve the average reporting speed by 87 days. CONCLUSIONS:Knowing the time elapsing at each step within the reporting process helps in prioritizing the needs and estimating the impact of future interventions. Where some previous studies focused on automating some of the cancer reporting activities, we anticipate much shorter reporting by leveraging health information technologies to target this waiting period.

SUBMITTER: Jabour AM

PROVIDER: S-EPMC5856936 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Toward Timely Data for Cancer Research: Assessment and Reengineering of the Cancer Reporting Process.

Jabour Abdulrahman M AM Dixon Brian E BE Jones Josette F JF Haggstrom David A DA

JMIR cancer 20180301 1

<h4>Background</h4>Cancer registries systematically collect cancer-related data to support cancer surveillance activities. However, cancer data are often unavailable for months to years after diagnosis, limiting its utility.<h4>Objective</h4>The objective of this study was to identify the barriers to rapid cancer reporting and identify ways to shorten the turnaround time.<h4>Methods</h4>Certified cancer registrars reporting to the Indiana State Department of Health cancer registry participated i ...[more]

PMID: 29496653

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Project description:BackgroundSuccessful implementation of pay-for-quality (P4Q) programs mostly depends upon a valid, timely, and reliable data about quality measures generated by providers, and interpreted by payers. The aim of this study was to establish a data reporting method for P4Q program through an action research.MethodsQualitative method was used to align theory with action through a three-cycle action research. The study was conducted in September 15, 2015 to March 15, 2017, in East-Azerbaijan, Iran. The purposeful sampling was used to select participants. The participants included healthcare providers, staff in district health centers (DHC), experts, and managers in the provincial primary health center (PPHC). Data was collected by interviews, focus group discussions, and expert panels. Content analysis was used to synthesize the data. In each step, decisions about data reporting methods were made through a consensus of expert panel members.ResultsThe most important dimensions of data reporting method were data entry and accuracy, data reporting, data analysis and interpretations, the flexibility of method, and training. By establishment of an online data reporting system for the P4Q program, a major improvement was observed in the documentation of performance data, the satisfaction of health care providers and staff (e.g. either in DHCs or PPHC), improvement of the P4Q program and acceptance of the P4Q program by providers. Following the present study, the online system was expanded in Iran's public health system for data collection and estimating the amount of incentive payments in P4Q program. Moreover, more improvements were achieved by linking the system to EMRs and also, providing automated feedback to providers about their own performance.ConclusionsA web-based computerized system with the capability of linking medical record and also its ability to provide feedback to healthcare providers was identified as an appropriate method of data reporting in the P4Q program from the viewpoints of participants in this study.

Project description:BackgroundSocial media have transformed the communications landscape. People increasingly obtain news and health information online and via social media. Social media platforms also serve as novel sources of rich observational data for health research (including infodemiology, infoveillance, and digital disease detection detection). While the number of studies using social data is growing rapidly, very few of these studies transparently outline their methods for collecting, filtering, and reporting those data. Keywords and search filters applied to social data form the lens through which researchers may observe what and how people communicate about a given topic. Without a properly focused lens, research conclusions may be biased or misleading. Standards of reporting data sources and quality are needed so that data scientists and consumers of social media research can evaluate and compare methods and findings across studies.ObjectiveWe aimed to develop and apply a framework of social media data collection and quality assessment and to propose a reporting standard, which researchers and reviewers may use to evaluate and compare the quality of social data across studies.MethodsWe propose a conceptual framework consisting of three major steps in collecting social media data: develop, apply, and validate search filters. This framework is based on two criteria: retrieval precision (how much of retrieved data is relevant) and retrieval recall (how much of the relevant data is retrieved). We then discuss two conditions that estimation of retrieval precision and recall rely on--accurate human coding and full data collection--and how to calculate these statistics in cases that deviate from the two ideal conditions. We then apply the framework on a real-world example using approximately 4 million tobacco-related tweets collected from the Twitter firehose.ResultsWe developed and applied a search filter to retrieve e-cigarette-related tweets from the archive based on three keyword categories: devices, brands, and behavior. The search filter retrieved 82,205 e-cigarette-related tweets from the archive and was validated. Retrieval precision was calculated above 95% in all cases. Retrieval recall was 86% assuming ideal conditions (no human coding errors and full data collection), 75% when unretrieved messages could not be archived, 86% assuming no false negative errors by coders, and 93% allowing both false negative and false positive errors by human coders.ConclusionsThis paper sets forth a conceptual framework for the filtering and quality evaluation of social data that addresses several common challenges and moves toward establishing a standard of reporting social data. Researchers should clearly delineate data sources, how data were accessed and collected, and the search filter building process and how retrieval precision and recall were calculated. The proposed framework can be adapted to other public social media platforms.

Project description:ObjectivesTo investigate the most common errors in residents' preliminary reports, if structured reporting impacts error types and frequencies, and to identify possible implications for resident education and patient safety.Material and methodsChanges in report content were tracked by a report comparison tool on a word level and extracted for 78,625 radiology reports dictated from September 2017 to December 2018 in our department. Following data aggregation according to word stems and stratification by subspecialty (e.g., neuroradiology) and imaging modality, frequencies of additions/deletions were analyzed for findings and impression report section separately and compared between subgroups.ResultsOverall modifications per report averaged 4.1 words, with demonstrably higher amounts of changes for cross-sectional imaging (CT: 6.4; MRI: 6.7) than non-cross-sectional imaging (radiographs: 0.2; ultrasound: 2.8). The four most frequently changed words (right, left, one, and none) remained almost similar among all subgroups (range: 0.072-0.117 per report; once every 9-14 reports). Albeit representing only 0.02% of analyzed words, they accounted for up to 9.7% of all observed changes. Subspecialties solely using structured reporting had substantially lower change ratios in the findings report section (mean: 0.2 per report) compared with prose-style reporting subspecialties (mean: 2.0). Relative frequencies of the most changed words remained unchanged.ConclusionResidents' most common reporting errors in all subspecialties and modalities are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). Structured reporting reduces overall error rates, but does not affect occurrence of the most common errors. Increased error awareness and measures improving report correctness and ensuring patient safety are required.Key points• The two most common reporting errors in residents' preliminary reports are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). • Structured reporting reduces the overall the error frequency in the findings report section by a factor of 10 (structured reporting: mean 0.2 per report; prose-style reporting: 2.0) but does not affect the occurrence of the two major errors. • Staff radiologist review behavior noticeably differs between radiology subspecialties.

Dataset Information

Toward Timely Data for Cancer Research: Assessment and Reengineering of the Cancer Reporting Process.

Publications

Toward Timely Data for Cancer Research: Assessment and Reengineering of the Cancer Reporting Process.

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