Review of Developments in Electronic, Clinical Data Collection, and Documentation Systems over the Last Decade - Are We Ready for Big Data in Routine Health Care?

Applying Data Warehousing to a Phase III Clinical Trial From the Fondazione Italiana Linfomi Ensures Superior Data Quality and Improved Assessment of Clinical Outcomes

PURPOSE

Data collection in clinical trials is becoming complex, with a huge number of variables that need to be recorded, verified, and analyzed to effectively measure clinical outcomes. In this study, we used data warehouse (DW) concepts to achieve this goal. A DW was developed to accommodate data from a large clinical trial, including all the characteristics collected. We present the results related to baseline variables with the following objectives: developing a data quality (DQ) control strategy and improving outcome analysis according to the clinical trial primary end points.

METHODS

Data were retrieved from the electronic case reporting forms (eCRFs) of the phase III, multicenter MCL0208 trial (ClinicalTrials.gov identifier: NCT02354313) of the Fondazione Italiana Linfomi for younger patients with untreated mantle cell lymphoma (MCL). The DW was created with a relational database management system. Recommended DQ dimensions were observed to monitor the activity of each site to handle DQ management during patient follow-up. The DQ management was applied to clinically relevant parameters that predicted progression-free survival to assess its impact.

RESULTS

The DW encompassed 16 tables, which included 226 variables for 300 patients and 199,500 items of data. The tool allowed cross-comparison analysis and detected some incongruities in eCRFs, prompting queries to clinical centers. This had an impact on clinical end points, as the DQ control strategy was able to improve the prognostic stratification according to single parameters, such as tumor infiltration by flow cytometry, and even using established prognosticators, such as the MCL International Prognostic Index.

CONCLUSION

The DW is a powerful tool to organize results from large phase III clinical trials and to effectively improve DQ through the application of effective engineered tools.

Retrospective analysis of breast radiotherapy treatment plans: Curating the 'non-curated'

INTRODUCTION

This paper provides a demonstration of how non-curated data can be retrospectively cleaned, so that existing repositories of radiotherapy treatment planning data can be used to complete bulk retrospective analyses of dosimetric trends and other plan characteristics.

METHODS

A non curated archive of 1137 radiotherapy treatment plans accumulated over a 12-month period, from five radiotherapy centres operated by one institution, was used to investigate and demonstrate a process of clinical data cleansing, by: identifying and translating inconsistent structure names; correcting inconsistent lung contouring; excluding plans for treatments other than breast tangents and plans without identifiable PTV, lung and heart structures; and identifying but not excluding plans that deviated from the local planning protocol. PTV, heart and lung dose-volume metrics were evaluated, in addition to a sample of personnel and linac load indicators.

RESULTS

Data cleansing reduced the number of treatment plans in the sample by 35.7%. Inconsistent structure names were successfully identified and translated (e.g. 35 different names for lung). Automatically separating whole lung structures into left and right lung structures allowed the effect of contralateral and ipsilateral lung dose to be evaluated, while introducing some small uncertainties, compared to manual contouring. PTV doses were indicative of prescription doses. Breast treatment work was unevenly distributed between oncologists and between metropolitan and regional centres.

CONCLUSION

This paper exemplifies the data cleansing and data analysis steps that may be completed using existing treatment planning data, to provide individual radiation oncology departments with access to information on their own patient populations. Clearly, the well-planned and systematic recording of new, high quality data is the preferred solution, but the retrospective curation of non-curated data may be a useful interim solution, for radiation oncology departments where the systems for recording of new data have yet to be designed and agreed.

Reliability of Paper-Based Routine Documentation in Psychiatric Inpatient Care and Recommendations for Further Improvement

Background: Health services research is of increasing importance in current psychiatry. Therefore, large datasets and aggregation of data generated by electronic routine documentation due to legal, financial, or administrative purposes play an important role. However, paper-based routine documentation is still of interest. It remains relevant in less developed health care systems, in emergency settings, and in long-term retrospective and historical studies. Whereas studies examining the reliability of electronic routine documentation support the application of routine data for research purposes, our knowledge regarding reliability of paper-based routine documentation is still very sparse. Methods: Basic documentation (BADO) was completed on paper forms and digitalized manually for all inpatients of the Department of Psychiatry and Psychotherapy, University Hospital Hamburg-Eppendorf, Germany, treated within the time period from 1998 to 2006. Four hundred twelve cases of first-episode psychosis patients were chosen for comparison with clinical data from paper-based patient files. The percentage of missing information, the percentage of correct classifications, sensitivity, and positive predictive value were calculated for all applicable variables. Results: In eight cases (1.9%), a BADO form was available, but was not filled in. In 37 cases (7.0%), the patient files were lost and could not be obtained from the centralized archive. Routine data were available for all other cases in 20 (58.8%) of the examined 34 variables, and the percentage of missing data for the remaining variables ranged between 0.3% and 22.9%, with only the variables education and suicidality during treatment having more than 5% missing data. In general, the overall rate of correct classifications was high, with a median percentage of 86.4% to 99.7% for the examined variables. Sensitivity was above 75% for eight and <75% but above 50% for six of the examined 17 variables. Values for the positive predictive value were above 75% for nine and <75% but above 50% for three variables. Conclusion: In summary, paper-based routine documentation reaches acceptable reliability, but this is dependent on the chosen documentation categories and variables. Based on the present findings, paper-based routine documentation can indeed be used for quality management, organizational development, and health services research. Its limitations, however, have to be kept in mind.

[Big data in imaging]

Until now, most major medical advancements have been achieved through hypothesis-driven research within the scope of clinical trials. However, due to a multitude of variables, only a certain number of research questions could be addressed during a single study, thus rendering these studies expensive and time consuming. Big data acquisition enables a new data-based approach in which large volumes of data can be used to investigate all variables, thus opening new horizons. Due to universal digitalization of the data as well as ever-improving hard- and software solutions, imaging would appear to be predestined for such analyses. Several small studies have already demonstrated that automated analysis algorithms and artificial intelligence can identify pathologies with high precision. Such automated systems would also seem well suited for rheumatology imaging, since a method for individualized risk stratification has long been sought for these patients. However, despite all the promising options, the heterogeneity of the data and highly complex regulations covering data protection in Germany would still render a big data solution for imaging difficult today. Overcoming these boundaries is challenging, but the enormous potential advances in clinical management and science render pursuit of this goal worthwhile.

Use of Multicenter Data in a Large Cancer Registry for Evaluation of Outcome and Implementation of Novel Concepts

Large clinical cancer registries (CCRs) in Germany shall be strengthened by the German Social Code Book V (SGB V) and implemented until the end of 2017. There are currently several large cancer registries that support clinical data for outcome analysis and knowledge acquisition. The various examples of the Munich Cancer Registry outlined in this paper present many-sided possibilities using and analyzing registry data. The main objective of population-based cancer registration within a defined area and the performance of outcomes research is to provide feedback regarding the results to the broad public, the reporting doctors, and the scientific community. These tasks determine principles of operation and data usage by CCRs. Each clinical department delivers its own findings and applied therapy. The compilation of these data in CCRs provides information on patient progress through the regional network of medical care and delivers meaningful information on the course of oncological diseases. Successful implementation of CCRs allows for presenting the statistical outcomes of health-care delivery, improving the quality of care within the region, accelerating the process of implementing innovative therapies, and generating new hypotheses as a stimulus for research activities.

Big Data in Designing Clinical Trials: Opportunities and Challenges

Emergence of big data analytics resource systems (BDARSs) as a part of routine practice in Radiation Oncology is on the horizon. Gradually, individual researchers, vendors, and professional societies are leading initiatives to create and demonstrate use of automated systems. What are the implications for design of clinical trials, as these systems emerge? Gold standard, randomized controlled trials (RCTs) have high internal validity for the patients and settings fitting constraints of the trial, but also have limitations including: reproducibility, generalizability to routine practice, infrequent external validation, selection bias, characterization of confounding factors, ethics, and use for rare events. BDARS present opportunities to augment and extend RCTs. Preliminary modeling using single- and muti-institutional BDARS may lead to better design and less cost. Standardizations in data elements, clinical processes, and nomenclatures used to decrease variability and increase veracity needed for automation and multi-institutional data pooling in BDARS also support ability to add clinical validation phases to clinical trial design and increase participation. However, volume and variety in BDARS present other technical, policy, and conceptual challenges including applicable statistical concepts, cloud-based technologies. In this summary, we will examine both the opportunities and the challenges for use of big data in design of clinical trials.

The big data effort in radiation oncology: Data mining or data farming?

Although large volumes of information are entered into our electronic health care records, radiation oncology information systems and treatment planning systems on a daily basis, the goal of extracting and using this big data has been slow to emerge. Development of strategies to meet this goal is aided by examining issues with a data farming instead of a data mining conceptualization. Using this model, a vision of key data elements, clinical process changes, technology issues and solutions, and role for professional societies is presented. With a better view of technology, process and standardization factors, definition and prioritization of efforts can be more effectively directed.

A critical review of recent developments in radiotherapy for non-small cell lung cancer

Lung cancer is the leading cause of cancer mortality, and radiotherapy plays a key role in both curative and palliative treatments for this disease. Recent advances include stereotactic ablative radiotherapy (SABR), which is now established as a curative-intent treatment option for patients with peripheral early-stage NSCLC who are medically inoperable, or at high risk for surgical complications. Improved delivery techniques have facilitated studies evaluating the role of SABR in oligometastatic NSCLC, and encouraged the use of high-technology radiotherapy in some palliative settings. Although outcomes in locally advanced NSCLC remain disappointing for many patients, future progress may come about from an improved understanding of disease biology and the development of radiotherapy approaches that further reduce normal tissue irradiation. At the moment, the benefits, if any, of radiotherapy technologies such as proton beam therapy remain unproven. This paper provides a critical review of selected aspects of modern radiotherapy for lung cancer, highlights the current limitations in our understanding and treatment approaches, and discuss future treatment strategies for NSCLC.