Regardless of industry, the ability to collect, manage, and intelligently leverage data will clearly be a differentiator for the foreseeable future. Executives in healthcare are acutely aware of the disruption being driven by this new paradigm and understand that this trend is impacting every sector, from banking to farming to manufacturing.
Ultimately, investing time and resources in data collection and analysis is only valuable if it provides insight for making proactive, tactical decisions. Innovative companies today are using big data and analytics to drive attributable revenue and compete more effectively.
Adoption Curves Vary
Healthcare has unfortunately been slow to adopt new technologies, including analytics, compared to other industries. Healthcare payers first used statistical modeling tools with large data sets, where the primary objective was to identify potential overpayment to providers based on certain variables. Payers can now be embracing data analytics as an entrée into machine learning and artificial intelligence technologies. More and more companies are realizing the power of capturing and managing healthcare-related information from a range of sources and using it to provide risk adjustment analytics, identify opportunities for retrospective chart reviews, support various data submission protocols and gain insight from clinical services, just to name a few.
The good news is that healthcare overall is definitely trending in the right direction from a big data perspective. IDC analysts recently predicted that 30% of healthcare providers will apply some form of analytics to patient data by 2018.
Heads up – the IoT is Here
As if the data deluge wasn’t daunting enough now, the impact of the Internet of Things is here. The next uptick in data creation will be exponentially larger – estimates are that by 2020 there will be 50 billion devices connected to the Internet of Things.
Once everything has an IP address, there is certainly greater potential for all kinds of dot-connecting, data analysis-driven innovation. But there is also the increased likelihood of malfeasance ranging from privacy invasion to security breaches. The increased data will make it that much more important to be able to separate noise from true insight.
How to proceed
Based on research, trending and current best practices, these steps could help improve overall success rates when deploying data analytics.
- Identify executive stakeholders: as is always the case with any transformational initiative, clearly identifying major stakeholders whose process and procedures (and perhaps P&L) will be impacted is critical. Selling in data analytics requires buy-in from senior leadership and a clear strategy for integrating data systems to demonstrate business value. Participants will likely range across disciplines and include colleagues from marketing, strategy and operations teams.
- Engage Information Technology: leadership from the infrastructure and tools side of the house must be able to manage the data capture and analytics processes. Clearly articulating the business value as well as the impact to existing processes and policies will be key.
- End user buy-in: end users must have a way to weigh-in on approach, timing, logistics, impact - especially when developing predictive models.
- Project Management Office (PMO): a cross-functional team with the correct stakeholder participation and structure can conduct an initial data audit, defining what to capture and where to find it as well as processes for managing ongoing analysis, reporting back to the business/stakeholders and developing predictive models that contribute business value going forward.
The bottom line is that every major business decision connected to driving revenue, controlling costs, or mitigating risks can be enhanced with data analytics, regardless of industry. Adopting and implementing this approach can’t be done overnight, but data analytics is a vital tool that can help companies remain competitive and achieve success. With robust data analytics in place and buy-in from important stakeholders, any organization can be positioned for strategic data-driven decision making.