Artificial Intelligence
Consulting
Development ServicesMost of healthcare IT executives are currently heavily focused on the meaningful use of technological solutions such as EHR development, eHealth systems upgrades, telemedicine, ICD-10 that actually overtake other experimental health IT projects, Big Data should still be on top of their priority list in 2016 and beyond, according to Reda Chouffani (@healthcareitguy) from TechTarget. Building highly efficient healthcare Big Data teams is one of the easy steps healthcare decision makers can take to bring eHealth technology to an ultimately new maturity level, add ease of use to the current and to-be-developed solutions and lay out the foundation for effective translation of data insights into healthcare business intelligence (BI).
Building Big Data teams is a great way for healthcare organizations and hospitals to experiment with innovative data discovery and visualization initiatives and enable successful execution of Big Data projects in health IT. Another critical role Big Data teams will play in digitalization of healthcare consists in creating and analyzing different technology use cases for Big Data through brainstorming, and research and development (R&D). The ability of healthcare organizations to outline specific use cases will provide clear goals visibility for the leadership approval and help prioritize IT projects to be implemented as part of their go-mobile, IoT and general software strategies.
It goes without saying that healthcare organizations simply won't be able to jump on the innovation train and deliver quality on-demand services to patients and other stakeholders without leveraging the power of data analytics. Big Data professionals are needed to build tools able to capture and analyze data generated by online healthcare systems such as telemedicine apps, decision aid engines, online booking systems, etc., and translate them into the action plans and smarter business decisions. But what's even more important - Big Data specialists will help the average users not skilled in analytics or technology (i.e. medical personnel and patients) easily understand data visualization and make proactive decisions without involving data scientists. A new generation of enterprise data discovery tools and platforms need thorough customization and deployment by Big Data talent, and a huge proportion of non digital native users should be trained properly on the usage of these systems. So, Big Data teams should be viewed as an inevitable addition to healthcare organizations' IT departments, not as a threat to the status quo.
Yet, Big Data in general and how to use it for healthcare benefits in particular is still very unclear to most healthcare leaders (just like in any other industry) today, and the lack of this understanding is a huge impediment to effective Big Data strategies. Pools of data science and analytics talent are only being built now, and how to embed them seamlessly to IT teams and gain best value from their skills remains a burning question.
Let's see how healthcare organizations can best attract, retain and utilize their Big Data talent to stay ahead of the curve even before Big Data goes mainstream.
1. Embed your data analysts directly on the R&D and problem solving teams of your healthcare organization
The best fit for Big Data talent in healthcare organizations is IT and/or service quality improvement team. The key Big Data functions to be integrated with your R&D and problem solving teams are:
- Data cleansing: your Big data specialist will make sure that all incoming data recorded in the system is clean and accurate and stays clean over the entire data lifecycle;
- Data exploration: your data analyst will sift through data mountains to explore the data users actually need and eliminate data that's not in use within your healthcare organization;
- Data architecture: your Big Data talent will put together all data discovered in the previous function, organize and structure it to facilitate understating and ease of use by the average users. They'll also configure your data discovery tool to allow your data to be updated every minute or hour depending on your specific needs;
- Data modelling: that's when your data scientist steps up to convert the organized data into sophisticated analytics models to help predict patient behavior or enable advanced segmentation of users and optimization of processes;
- Data management: that's when your data analysts turn the data models into the actual results. Having in-depth knowledge of your eHealth systems and tools, the data specialist will be able to prioritize channels and sequence of Big Data based projects. For instance, based on the analysis of historical behavior of an identified user segment (i.e. patients with mental disorders), your data analyst will be able to suggest on the best time and frequency of mobile notifications to be sent to this particular segment to remind them of medical check-ups, use of medications, etc. In case of telemedicine solution, for example, data analysts can determine the best time for doctors to call their patients based on their lifestyle and habits.
On large health IT projects, it's recommended that you assign each of the above functions to a different person to avoid burn-out and achieve maximum efficiency, while all of these roles can actually be assigned to one or two Big Data specialists on small project teams.
2. Enable your analytics professionals
Make sure to provide all of the required Big Data tools to your analysts to make their work environment both challenging and intelligent. You should talk to each of your data team members and ask them what tooling they need to do their work effectively and benefit your organization. Some data discovery tools require purchasing add-ons or additional modules such as statistics package, etc. Remember that failing to provide required tools to your data talent will first and foremost result in misleading and inaccurate outcomes for your healthcare organization!
3. Encourage your analytics teams to innovate
Analytics specialists should experiment with new tools and data discovery and presentation techniques as this is a relatively new landscape that's lacking major use cases. Make sure to foster creativity and better problem solving by encouraging your Big Data teams to innovate and embed new layers of business intelligence within data dashboards rather than sticking to the old paradigms. You should also allocate some budget for data analysts' training and education to help them stay in line with the latest Big Data trends and better overcome challenges.
And how are you leveraging your Big Data Talent?
Source: TechTarget, 2015
