While other industries, such as retail, have been quick to embrace big data, healthcare and pharmaceutical companies are beginning to unlock its value. Many are just now beginning to use analytics to increase competitiveness, fine-tune sales pipelines, and accelerate R&D and product development.
Analysts predict the global healthcare analytics market is likely to grow to $36 billion in 2022, up from $8.6 billion in 2015. And applying big-data strategies to better inform decision making could pay off for firms: It has the potential to generate $100 billion in value annually across the US healthcare system, according to the McKinsey Global Institute.
Healthcare, biotech, and pharma companies are beginning to integrate big data analytics throughout their value chain.
Already, healthcare, biotech, and pharma companies are beginning to integrate big data analytics throughout their value chain to improve efficiency, increase revenue and generate better healthcare outcomes. Here are a few examples of how they’re implementing new data programs across functions.
1. Improve Patient Care
Some physician groups are using predictive analytics to improve patient care. For example, Penn Medicine launched a program that uses patient data to help doctors identify at-risk heart failure patients before they’ve had a negative event. The program uses a heart failure detection algorithm to predict which patients might be at risk. This intelligence has enabled doctors to improve the care of these patients and, as a result, reduce readmission rates.
2. Drug Development
Predictive modeling is a promising approach for drug development. It’s used to analyze existing data to identify potential new drugs that are safer and more effective. During clinical trials, data analysis can be used to identify patients that best fit the study requirements. The outcome? Shorter, smaller and less expensive trials.
Big data can transform sales data points into insights.
3. Improve Sales Effectiveness
Data analysis can also play a significant role in boosting sales team results. Pfizer harnesses sales data and transforms it into insights that inform its sales performance and strategy. They can evaluate sales pitches and gain a better understanding of how drugs are performing in different markets.
4. Risk Adjustments
Medicare risk adjustments allow insurance companies to charge more for certain services, but they often don’t for a variety of reasons. But as big data and natural language processing tools improve the process of evaluating unstructured data, such as claims, insurance companies can more accurately adjust risk. This leads to higher payments and profits, while also eliminating potential penalties for improper payments. Another equally critical benefit: it can pinpoint gaps in care, which leads to improved health outcomes.
Healthcare and pharma firms may hesitate to launch new data analysis programs because they don’t have anyone on staff with the right expertise to launch and manage the projects. If lack of in-house knowledge is holding you back, engaging an agency that specializes in data talent can accelerate your program and maintain it over time.
The key advantage of specialized talent agencies in tight labor markets is the large network of hard-to-find data experts, especially those that specialize in data collection, modeling, and machine learning and who have expertise at the highest level of the organization. For example, IQ Workforce’s data analytics network includes thousands of data scientists who’ve worked across the spectrum, with expertise from point of sale to business intelligence. While 48% have worked at the managerial or director level, 8% have executive level experience.
And, as companies determine how they can use sales, patient, and other data to drive revenue, they can work with talent agencies to bring in highly skilled and experienced data analytic professionals, either for simple one-off projects or to lead entire data initiatives.