Enterprise-Wide Clinical Data Integration

1997 ◽  
pp. 41-54 ◽  
Author(s):  
Michael G. Kahn
Keyword(s):  
Data Integration ◽  
Clinical Data

Economics of Clinical Data Integration

2018 ◽  
pp. 267-287
Author(s):  
Stephen Foreman ◽  
Joseph Kilsdonk ◽  
Kelly Boggs
Keyword(s):  
Data Integration ◽  
Clinical Data

scholarly journals Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Jannik Schaaf ◽  
Martin Sedlmayr ◽  
Johanna Schaefer ◽  
Holger Storf
Keyword(s):  
Decision Support ◽  
Data Integration ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Data ◽  
Rare Diseases ◽  
Scoping Review ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

Abstract Background Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. Methods We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items “Objective and background of the publication/project”, “System or project name”, “Functionality”, “Type of clinical data”, “Rare Diseases covered”, “Development status”, “System availability”, “Data entry and integration”, “Last software update” and “Clinical usage”. Results The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: “Analysis or comparison of genetic and phenotypic data,” “machine learning” and “information retrieval”. Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. Conclusions Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

scholarly journals Computer Vision for Continuous Bedside Pharmacological Data Extraction: A Novel Application of Artificial Intelligence for Clinical Data Recording and Biomedical Research

2021 ◽  
Vol 4 ◽  
Author(s):  
Logan Froese ◽  
Joshua Dian ◽  
Carleen Batson ◽  
Alwyn Gomez ◽  
Amanjyot Singh Sainbhi ◽  
...  
Keyword(s):  
Computer Vision ◽  
Data Integration ◽  
Real Time ◽  
Clinical Data ◽  
Biomedical Research ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Time Data ◽  
Optical Character ◽  
Real Time Data

Introduction: As real time data processing is integrated with medical care for traumatic brain injury (TBI) patients, there is a requirement for devices to have digital output. However, there are still many devices that fail to have the required hardware to export real time data into an acceptable digital format or in a continuously updating manner. This is particularly the case for many intravenous pumps and older technological systems. Such accurate and digital real time data integration within TBI care and other fields is critical as we move towards digitizing healthcare information and integrating clinical data streams to improve bedside care. We propose to address this gap in technology by building a system that employs Optical Character Recognition through computer vision, using real time images from a pump monitor to extract the desired real time information.Methods: Using freely available software and readily available technology, we built a script that extracts real time images from a medication pump and then processes them using Optical Character Recognition to create digital text from the image. This text was then transferred to an ICM + real-time monitoring software in parallel with other retrieved physiological data.Results: The prototype that was built works effectively for our device, with source code openly available to interested end-users. However, future work is required for a more universal application of such a system.Conclusion: Advances here can improve medical information collection in the clinical environment, eliminating human error with bedside charting, and aid in data integration for biomedical research where many complex data sets can be seamlessly integrated digitally. Our design demonstrates a simple adaptation of current technology to help with this integration.

scholarly journals Data Integration to Improve Real-world Health Outcomes Research for Non–Small Cell Lung Cancer in the United States: Descriptive and Qualitative Exploration

JMIR Cancer ◽  
10.2196/23161 ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e23161
Author(s):  
Michael Grabner ◽  
Cliff Molife ◽  
Liya Wang ◽  
Katherine B Winfree ◽  
Zhanglin Lin Cui ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Data Integration ◽  
Clinical Data ◽  
Real World ◽  
Administrative Claims ◽  
Data Set ◽  
Health Outcomes Research ◽  
Data Source ◽  
The Mean

Background The integration of data from disparate sources could help alleviate data insufficiency in real-world studies and compensate for the inadequacies of single data sources and short-duration, small sample size studies while improving the utility of data for research. Objective This study aims to describe and evaluate a process of integrating data from several complementary sources to conduct health outcomes research in patients with non–small cell lung cancer (NSCLC). The integrated data set is also used to describe patient demographics, clinical characteristics, treatment patterns, and mortality rates. Methods This retrospective cohort study integrated data from 4 sources: administrative claims from the HealthCore Integrated Research Database, clinical data from a Cancer Care Quality Program (CCQP), clinical data from abstracted medical records (MRs), and mortality data from the US Social Security Administration. Patients with lung cancer who initiated second-line (2L) therapy between November 01, 2015, and April 13, 2018, were identified in the claims and CCQP data. Eligible patients were 18 years or older and received atezolizumab, docetaxel, erlotinib, nivolumab, pembrolizumab, pemetrexed, or ramucirumab in the 2L setting. The main analysis cohort included patients with claims data and data from at least one additional data source (CCQP or MR). Patients without integrated data (claims only) were reported separately. Descriptive and univariate statistics were reported. Results Data integration resulted in a main analysis cohort of 2195 patients with NSCLC; 2106 patients had CCQP and 407 patients had MR data. The claims-only cohort included 931 eligible patients. For the main analysis cohort, the mean age was 62.1 (SD 9.27) years, 48.56% (1066/2195) were female, the median length of follow-up was 6.8 months, and for 37.77% (829/2195), death was observed. For the claims-only cohort, the mean age was 66.6 (SD 12.69) years, 52.1% (485/931) were female, the median length of follow-up was 8.6 months, and for 29.3% (273/931), death was observed. The most frequent 2L treatment was immunotherapy (1094/2195, 49.84%), followed by platinum-based regimens (472/2195, 21.50%) and single-agent chemotherapy (441/2195, 20.09%); mean duration of 2L therapy was 5.6 (SD 4.9, median 4) months. We describe challenges and learnings from the data integration process, and the benefits of the integrated data set, which includes a richer set of clinical and outcome data to supplement the utilization metrics available in administrative claims. Conclusions The management of patients with NSCLC requires care from a multidisciplinary team, leading to a lack of a single aggregated data source in real-world settings. The availability of integrated clinical data from MRs, health plan claims, and other sources of clinical care may improve the ability to assess emerging treatments.

scholarly journals Data Integration to Improve Real-world Health Outcomes Research for Non–Small Cell Lung Cancer in the United States: Descriptive and Qualitative Exploration (Preprint)

2020 ◽  
Author(s):  
Michael Grabner ◽  
Cliff Molife ◽  
Liya Wang ◽  
Katherine B Winfree ◽  
Zhanglin Lin Cui ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Data Integration ◽  
Clinical Data ◽  
Real World ◽  
Administrative Claims ◽  
Data Set ◽  
Health Outcomes Research ◽  
Data Source ◽  
The Mean

BACKGROUND The integration of data from disparate sources could help alleviate data insufficiency in real-world studies and compensate for the inadequacies of single data sources and short-duration, small sample size studies while improving the utility of data for research. OBJECTIVE This study aims to describe and evaluate a process of integrating data from several complementary sources to conduct health outcomes research in patients with non–small cell lung cancer (NSCLC). The integrated data set is also used to describe patient demographics, clinical characteristics, treatment patterns, and mortality rates. METHODS This retrospective cohort study integrated data from 4 sources: administrative claims from the HealthCore Integrated Research Database, clinical data from a Cancer Care Quality Program (CCQP), clinical data from abstracted medical records (MRs), and mortality data from the US Social Security Administration. Patients with lung cancer who initiated second-line (2L) therapy between November 01, 2015, and April 13, 2018, were identified in the claims and CCQP data. Eligible patients were 18 years or older and received atezolizumab, docetaxel, erlotinib, nivolumab, pembrolizumab, pemetrexed, or ramucirumab in the 2L setting. The main analysis cohort included patients with claims data and data from at least one additional data source (CCQP or MR). Patients without integrated data (claims only) were reported separately. Descriptive and univariate statistics were reported. RESULTS Data integration resulted in a main analysis cohort of 2195 patients with NSCLC; 2106 patients had CCQP and 407 patients had MR data. The claims-only cohort included 931 eligible patients. For the main analysis cohort, the mean age was 62.1 (SD 9.27) years, 48.56% (1066/2195) were female, the median length of follow-up was 6.8 months, and for 37.77% (829/2195), death was observed. For the claims-only cohort, the mean age was 66.6 (SD 12.69) years, 52.1% (485/931) were female, the median length of follow-up was 8.6 months, and for 29.3% (273/931), death was observed. The most frequent 2L treatment was immunotherapy (1094/2195, 49.84%), followed by platinum-based regimens (472/2195, 21.50%) and single-agent chemotherapy (441/2195, 20.09%); mean duration of 2L therapy was 5.6 (SD 4.9, median 4) months. We describe challenges and learnings from the data integration process, and the benefits of the integrated data set, which includes a richer set of clinical and outcome data to supplement the utilization metrics available in administrative claims. CONCLUSIONS The management of patients with NSCLC requires care from a multidisciplinary team, leading to a lack of a single aggregated data source in real-world settings. The availability of integrated clinical data from MRs, health plan claims, and other sources of clinical care may improve the ability to assess emerging treatments.

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