Healthcare organizations have been investing heavily in big data analytics, software, hardware, staff, and services to get insights into quality and cost. These insights will be critical as healthcare facilities take on more and more risk in a value-based care model. About 40 percent of healthcare providers are expanding IT budgets, a recent IDC Health Insights report revealed. The report noted that analytics is the top reason for the increase.
However, building an analytics-driven practice is just a midway point to the insights that healthcare organizations need to fully leverage their data investments. This mindset of healthcare organizations that they can gain insights from EMRs and other available data often proves to be a mistake for many of them. The reason is simple: data analytics is outside the core competency of the organization, and those investment dollars could be spent in a more productive fashion.
Big data analytics can provide actionable insights to meaningfully, if not dramatically, improve economic and health outcomes. The faster an organization can reach those insights, the faster it will impact population health and quality. These changes will ultimately improve the bottom line by identifying key operating areas to reduce hospital readmissions, fraud and risk; improve customer experience, satisfaction, and pre- and post-treatment effectiveness.
Here are three common roadblocks for time-to-value of data analytics to insights.
The potential to realize health and operational improvements depends on the quantity, quality, velocity and variety of the data. In addition, it requires considering how to overcoming these common barriers:
Practical application of new knowledge from big data analytics is a skill.
Predictive analytics and precision medicine, for example, are discussed often, but the practice of creating new knowledge out of large amounts of data is easier said than done. Determining the best actions from data analysis requires the right mix of experts to validate and implement findings. Successful teams must blend data scientists with clinicians, company policymakers, and patients to make data truly valuable and actionable. To acquire this skill internally, an organization must be prepared to make a significant time and financial commitment.
A Deep-dive Into Specialty Pharma
A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.
There is a healthcare informaticist gap.
There are currently fewer than 15,000 formally trained medical informaticists in the U.S., with demand increasing up to 70,000 in the next five years. This creates a huge talent gap, where healthcare organizations lack the clinical expertise required to make data effective. Without informaticists to bridge the business and clinical aspects of data, it will be difficult for organizations to ask the right questions, let alone getting to the right actions and conclusions.
The traditional technology delivery cycle is too slow.
Most healthcare organizations have experienced the pain of implementing a new EMR system and understand the complexity, expense and time needed for an undertaking of that size. The typically slow health IT delivery cycle prolongs an organization’s time-to-value and virtually eliminates its ability to nimbly respond to market or policy changes.
Invest in a faster approach: Insights-as-a-service.
Insights-as-a-service allows organizations to effectively outsource the analytics factor of the data insights equation. A third party focused on data insights can more easily recruit data scientists, work with a clinical team, and hypothesize insights and actions at a much faster rate than in-house staff that may not have proper data analytics skills. In addition to staffing advantages, the external partner has the hardware, software, tools, and access to external data sources that is required to be successful.
Consider this scenario. A healthcare organization employee has the idea in June that adding hyperlocal weather data to the predictive model for clinic staffing may reduce wait times and overtime costs during the upcoming flu season. Even at the most rapid clip of vetting the idea internally for proof-of-concept (POC), gaining funding to do the POC and then proving the idea, it will take six to eight months. If the project started in June, the company is midway through the flu season before implementation — effectively delaying the ROI of the effort until the next flu season.
What if the organization had a technology partner that could leverage its existing analytics infrastructure and data license for weather in just three months? The insight could pay dividends in the upcoming flu season by reducing labor costs and improving patient experience. Investing your company resources in these types of partnerships is investing in business value, as opposed to building an entire infrastructure to answer the original question.
The path forward
There are many analytics companies available to healthcare organizations, but it is key to look for those that can easily integrate useful third-party data to existing data to add value to the insight and the investment. These types of deals are in their infancy, but if organizations can shift their mindset from building infrastructure to purchasing data insights, it will yield a tremendous competitive advantage and ability to scale actionable insights quickly.
Photo: goir, Getty Images
