Advanced analytics is transforming virtually every industry. Banking institutions have refined strategies for mitigating risk associated with loans, cybersecurity experts are tracking and predicting cyber criminals’ next moves, and the retail industry has nearly perfected consumer recommendations. Despite the enormous potential to leverage advanced analytics, the healthcare industry has struggled to achieve the right utilization to make significant advances in outcomes in business, finance, or patient care.
Static or Manual Models Won’t Cut It
Analytics isn’t new to the healthcare industry. But confusion in the market about exactly what advanced analytics is and how it can or should be used has slowed adoption and fueled debates over whether the industry is prepared to introduce and integrate such a capability.
The vast majority of today’s models require manual updating and can therefore cause unknown risk. This approach to advanced analytics does not account for the full potential of the computing power available today and is not equipped to solve for the wide variety of evolving goals the healthcare industry must achieve. Consider the cost, clinical, financial, and quality outcomes that must be measured. Not only do these goals not align, in many ways they are inherently in competition with each other. As such, efforts to manually adapt these existing models have proven to be a drain of resources for healthcare technology.
The promise of advanced analytics in healthcare can be boiled down to a lot of complexity, with little understanding about exactly how it will be used to provide meaningful value. Healthcare technology, specifically as it relates to advanced analytics, is insufficient in its current state. Healthcare technology solutions are in dire need of a jolt to the system in order to catch up and reap tangible benefits from the data at hand.
Prescription for Improving Healthcare Analytics
Igniting change in the industry requires a critical examination of three major components of the advanced analytical strategy: the technology, computing power, and the utilization of resources.
Technology. Even if organizations have access to thousands of models, they don’t have the manpower to improve or expand upon those models to account for constant inflows of fresh data or changes within existing data sets. Moving forward, automated advanced analytics capabilities will need to be considered as part of overall data strategies to take full advantage of existing investments. While some existing models may be out of date, many of the data tools employed by organizations today can be enhanced and re-energized through the use of automated advanced analytics by integrating methods like artificial intelligence and machine learning.
When it comes to volume, healthcare technology companies will need to shift the mindset that a single or handful of models will be sufficient. To account for the growing number of and increasingly complex goals that healthcare providers must address, the number of models being generated should multiply, even exponentially in some cases. The challenge here is that, in the current state of healthcare technology, managing and updating such a vast number of models would be nearly impossible. To support such an increase in models, automation must be incorporated into the process.
Computing power. Advances in computing power will continue to dictate the level at which advanced automated analytics can be successful. Computer processing power has a long way to go to match the human brain. But assuming that computing power will continue to increase at a rate predicted by Moore’s Law, the amount of interaction and possibilities with machine automation will expand tremendously.
The ability to automatically calibrate models based on changes in the environment is essential, and organizations must prepare themselves and their technology investments to account for and incorporate advances in computing power.
Resources. Data scientists are still hard to come by, and their salaries require a serious investment. By speeding up and automating the model-building process, healthcare technology providers can free up time for those valuable employees to focus on higher-impact missions. With quicker access to information, data scientists have the resources to expand upon their role. This revised view of a data scientist’s role allows more time to focus on delivering information to decision makers, effectively putting powerful information into the hands of the entire organization.
Contrary to misconceptions in the market today, the intent of adding artificial intelligence or machine learning to the mix is not to replace or compete with the human mind, but to act as a supplement. Admittedly, the balancing act between human and machine interaction is a delicate one, but through the ability to validate and override models, humans are able to work in tandem with machines, with each asking questions of the other to achieve the most powerful results.
Making technology part of the ecosystem will empower organizations that don’t have data scientists on staff to accelerate the modeling process, and organizations with a data science staff will enable those skilled workers to make more productive use of their time and talents.
Rob Patterson leads all go-to-market strategy, marketing, M&A, and strategic technology partnerships at ColdLight. Rob has successfully built highly disruptive and creative strategies at various technology companies around the world. Prior to ColdLight, Rob held senior marketing positions at Qlik, where he built customer marketing teams globally and worked to evangelize the QlikView platform with enterprise clients. Prior to Qlik, Rob ran marketing programs in the Mid-Atlantic States District for Microsoft. Rob holds a degree in Food & Retail Marketing from Saint Joseph’s University.
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