business intelligence in healthcare

For a few years now, healthcare leaders have been leveraging everything from patient data to operational data to improve decision-making. According to a whitepaper published by Tableau, business leaders in the healthcare industry are using analytics across the following areas:  

  • Clinical BI: This revolves around using clinical data to make better decisions regarding patient care  
  • Operational BI: Using data from operations to improve administrative processes, drive efficiency of operations and utilise resources effectively  
  • Financial BI: To use both patient data and operational data to drive up financial metrics, including profitability, revenue growth, return on capital employed, and generate more free cash flow  

In this blog, we discuss key trends in healthcare analytics and BI. Additionally, we also highlight the role of data storytelling and data visualisation to ensure data and insights are shared better across the organisation, so enterprise-wide decision making gets better.  

Key BI and Analytics Trends in the Healthcare Industry  

While the healthcare industry is not new to analytics, there are a few trends shaping the future of BI in healthcare:  

Actionable Insights: Healthcare leaders are emphasising the need for actionable insights which can be put to use immediately. Here are several examples of insights that are immediately useful.  

  • Predictive treatment planning based on patient symptoms, using past data  
  • Scheduling staff based on predicted volume of patients  
  • Analytics for better inventory management to improve the efficiency of the supply chain (directly impacts cash flow and profitability) 
  • Medical device purchase decisions based on data  
  • Marketing budget allocation based on past ad campaign effectiveness  
  • Identify patients who may need a follow-up, based on analysing their clinical data  

Business Intelligence specialists are striving to transform healthcare organisations to become more and more insights-driven but to truly transform healthcare it is critical to understand the value of making quick decisions and “taking action.” 

Data Storytelling: While data is extremely important, for business users and people in the trenches of the healthcare industry, the real value lies in quickly culling out insights. With Data Storytelling, time taken from “data-to-insights” decreases and business users are able to quickly spot trends and opportunities.  

Data Storytelling is all about narratives and visualisations. The fundamental data can be of several types – descriptive, predictive, or prescriptive. With narratives, we are able to say something like, given this data and context, this is what has to be done. For instance, the future of Business Intelligence in healthcare will include a predictive insight that says, “for patients in the 30-40 age group, with symptoms A, B, and C,” these are some treatment options. The next step will obviously involve expert doctors picking and choosing the right way forward. But they have a head start with data storytelling.  

Governed Data Curation: There is no doubt that there are several regulations around privacy and protection of patient data. Your BI stack must be designed to ensure these regulatory requirements are met without leaving anything to chance. Complete HIPAA, PCI, GDPR, CCPA, and SOC 2 compliance is critical. Failure to do so can result in huge penalties and fines, not to mention several operational hassles.  

The BI platform you choose for your healthcare enterprise must make data security, data governance, and PII compliance easy to do.  

The Role of Industry Data Intelligence in Healthcare  

While company-specific data is important, it is also important for business leaders to benchmark their own companies with industry-wide performance. They need to stay ahead of the curve in terms of patient safety, healthcare experience provided, product and service innovation, etc.  

In order to do this, it is critical that they not only look at internal data but also capture external data and use that for analytics. Over the last few months, vaccinating the world against Covid-19 was the main priority. Now the time has come to accelerate non-Covid care.  

Additionally, the role of AI and machine learning in healthcare is becoming more mainstream. A data science expert at Merit says, “As in the case of any AI algorithm, training data is critical to make sure the AI model works as expected. And, well-designed data harvesting, data curation, and data cleaning processes are at the core of this workflow.”  

The Economist Intelligence Unit published the Healthcare 2022 report to capture major trends shaping the healthcare industry. The report highlighted everything from pharma supply chain challenges to regulations impacting the MedTech sector. It also touched upon the role of insurance companies and its impact on healthcare costs.  

Overall, decision-makers in this industry need to stay abreast of macro trends – to identify risks, spot opportunities, and plan for the future. Therefore, a company’s BI stack must ensure that these macro indicators and external data points (along with well-curated datasets) are fed as an input into your analytical models.  

How Merit can help with your Healthcare BI Journey  

At Merit, we are specialists in collecting high-volume, accurate industry-specific data, at scale and speed. Driven by our own data engine Data Xtractor, Merit enables rapid data collection, transformation, and ingestion from a diverse range of disparate online sources. 

Additionally, our data science and BI teams are specialists in several BI tools and technologies, and in partnership with our healthcare domain experts, we’re able to provide context-specific advice to a business situation your organisation is in. 

Also, our data engineers make it easy for industry intelligence firms to automate the following processes: 

  • Harvest, aggregate, and manage data, images, and narrative content from thousands of web sources 
  • Handle data in various formats – HTML, APIs, JSON, Excel, PDFs, PowerPoint 
  • Refine the raw data into a consistent and searchable format 
  • Enhance data with additional research to deliver value-added insights 
  • Setup the underlying data and information flow using the right data lake and data warehousing architecture 

To know more about our data practice, visit:

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