GETTING BACK ON TRACK IN RECORD TIME: OPTIMIZING A VISUAL ANALYTICS PROGRAM AND PROCESS

Transcription

1 GETTING BACK ON TRACK IN RECORD TIME: OPTIMIZING A VISUAL ANALYTICS PROGRAM AND PROCESS THE KEY QUESTION: How much can the power of visibility influence the decisionmaking process? The Background It s no surprise that clinical trials continue to generate substantial amounts of patient data from a wide variety of sources including electronic data capture, business process flow mapping and external vendor data, among many others. All this information must be collected, reviewed, cleaned, aggregated, analyzed and assimilated into reports that the multidisciplinary team, including scientists and medical personnel, use to monitor in-motion progress, data quality and patient safety during a given clinical trial or trials. Advances in technology and more specifically business intelligence and visual analytics tools and associated techniques powered by innovative platforms such as TIBCO Spotfire are revolutionizing the management of data for clinical trials

2 and beyond. With the ability to visualize multisource data including those previously untapped, clinical and medical personnel can achieve faster insights into the data and hone in on key areas to facilitate expedited review and decision making and thereby improve trial safety, quality and efficiency. The Specific Situation Although the clinical programming and data management teams at a top-tier multinational biopharmaceutical company worked with various analytical reporting tools, they found the tools to be inefficient for suporting processes like centralized monitoring and lacking the ability to interact and explore aggregated data in a self-service manner. As part of its investment in a more sophisticated tool set, this company added Spotfire to serve its visual analytics needs. However, lacking experienced in-house resources, this company did not have the bandwidth or capability to implement a solid process to develop and quality-check all Spotfire outputs cohesively in a package 1 of information. In the absence of a defined standard Spotfire package, determining which domains or datasets to include, the data relations within those sets and the desired graphical output were tasks that had to be created anew each time a trial was undertaken. As a result, the time and effort to create a visual analytics package often took weeks or months. 1 A Spotfire Package is defined as follows: PACKAGE PAGE/ PAGE GROUP VISUALIZATION A collection of pages packaged in one file A page is a page/tab within a package that contains one or more visualizations A page group is a group of pages defined by the name of the page A group of pages defined by the name of the page

3 Objectives The client wanted to make visual analytics an integrated tool that the clinical scientists and medical personnel could use in the data review and decision-making process to improve oversight for patient safety and accelerate insight for risk-based monitoring purposes with actionable outcomes. For this to happen, the client needed a comprehensive process road map, including a standard visual analytics package template, as well as reliable centralized collection, analysis and storage of data. This company also needed to educate end users in the employment of visual analytics to ensure the most effective visual tools were developed for each requirement and business use case. Challenges End users did not have enough knowledge of, or experience with, Spotfire to articulate their requirements in ways the programmers could understand and act on; in effect, they spoke different languages. The breakdown in communication between end users and programmers resulted in unmet requirements and suboptimal tool usage. Approach The client approached Chiltern to assist in providing an outsourcing model to leverage its technology solution. Under a functional service provider (FSP) agreement, Chiltern initially allocated one Spotfire programmer to assess the situation but quickly expanded the assignment to 11 programmers to help develop a comprehensive visual analytics programming solution. Using this approach, the client was able to apply highly experienced, dedicated, results-oriented individuals to the task without the extra economic burden associated with adding to its corporate headcount. The first step was to establish a baseline, ascertain where the implementation of the new visual analytics tool stood and perform a needs assessment. Requirements gathering followed in quick succession through the employment of frequent meetings between the end users and the programmers. Seemingly for the first time, both sides had an opportunity to understand precisely what was being requested of the other. The collective group sought to outline their needs in a more refined and prescriptive manner. An end user who may have previously asked to see all out-of-range lab results, could now ask for a scatter plot showing the results for all lab results that are out of range, with demographics indexed by color and con-meds indexed by shape. It should be noted that such meetings became the norm with agreement that any further development would only occur after congruence was achieved on the need. Now conversant with the language and capabilities of Spotfire, the group worked as a team to develop a standard visual analytics package based on requirements that could be applied to each and every trial. The team determined the standard study data tabulation model (SDTM) domains and associated data variables or datasets to include, as well as the data relations within those datasets. For example, the standard visual analytics package could now include demographics, adverse events and concomitant medications, with descriptions of how the data in each of those domains are to be visualized and a determination of which domains need to be visualized together. Once the standard package was agreed upon and implemented, the group worked

4 to develop a patient tracking tool with specific visualizations to support clinical monitoring for trials in different therapeutic areas. The following represents the sequence of events described above and the interactive communications with the client that resulted in a substantial portfolio and resourcing growth model that remains intact today. This optimized information sharing and set clear expectations so that both the requestors and the developers understood the needs INTERACTIVE CUSTOMER COMMUNICATION REGULAR MEETINGS INSTIGATED PROCESS COMPLIANCE RESOURCE ALLOCATION REQUIREMENTS GATHERING DEDICATED RESOURCE ALLOCATION CONDUCTED A NEEDS ASSESSMENT PROCESS CHANGE PORTFOLIO GROWTH No package template Lack of established turnaround time for package creation End user lack of experience resulting in incomplete requirements and translation of same into visualization No development occurred without first meeting with all parties for needs assessment and requirements gathering Outcomes Interactions and throughputs improved dramatically once end users and programmers had a better understanding of each other s needs and their mutual goals. Requirements became clear and complete, and the turnaround time to develop a standard visual analytics package was significantly reduced. The improved data review timing and decision-making capacity allowed faster access to the

5 medical personnel and data review boards, among others. Together, end users and programmers eliminated redundancies and consolidated multiple tools/technologies to satisfy several data review requirements. The client quantified the return of investment (ROI) throughout this process and found it to be significant. The ROI is based off the use of the client s template for data management for medical reviewers, clinical/medical monitors and biostatisticians, all of whom were using the same visualizations/package to check different levels of the data in a layered workflow. With Chilterns s involvement, more than 60 trials comprising more than 1,200 visualization pages and 6,000 visualizations have been completed and that number is climbing. Additionally, a clean patient tracker tool within Spotfire, in support of clinical monitoring, was developed and deployed for standard use. Conclusion Visual analytics enables data insights perhaps not possible with other technology tools. Exploring and analyzing data to discern trends or patterns visually allow users to interact with data in new ways, enabling an enhanced understanding of data. Visual analytics tools may be applied in a number of business areas including: Clinical trial data in support of data review and risk-based monitoring Business applications, including process flow mapping and financial trends Overall metrics management and project execution Visual analytics requires programming specialization and operational aptitude enhanced with hands-on training and experience. Tapping into Chiltern s expertise and resources in this area enhanced and improved the efficiency of the client s management of data and its analysis. As an industry leader in clinical analytics technology, Chiltern strategically integrates independent analytics tools such as BOXI, JReview, SAS, Cognos and Spotfire. Chiltern s programmers and analysts excel at working through the visual analytics process from end to end, beginning with a needs assessment and including process mapping, checking and hypothesizing and publishing the resulting data to a dashboard. Chiltern s philosophy of visual analytics is founded on these principles: a strong design and programmatic interface including features to support team development and allowing reuse of common components; an IT infrastructure and server environment that provides enterprise-level deployment options, such as security, access control and user management; and high-performance data processing. The underlying goal is not only to provide data, but to provide solutions Designed Around You, which yield the right data and information. Asia-Pacific & Europe: +44 (0) Latin America & North America: Info@Chiltern.com