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Unpacking Business Intelligence for Healthcare

Published 2021-01-18 08:21:57

 

Business Intelligence (BI) solutions are built to assist executives and managers in making informed business decisions that eliminate waste, help respond to changing market conditions, and ultimately drive increased revenues through efficient operational performance. By using technology-driven methods, historical and present data from business activities are analysed to deliver actionable insights using an array of techniques and tools such as data mining, visualisation, BI dashboards, and reports.

In practice, if you were to operate a lemonade stand that sold lemonade with honey and lemonade with sugar, business intelligence would help provide reports on which type of lemonade sold better. From this data, your lemonade business could adjust its production, sales, and marketing strategies to focus on the most profit-bearing lemonade based on the historical data.

There are 11 sectors based on the Global Industry Classification Standard (GICS), with these sectors broken down further into 24 industry groups, 69 industries and 158 sub-industries. Nearly all industries across the globe use BI to guide business decisions. In the finance industry, BI is a tool that allows banks and financial service providers to assess financial risk, analyse investment strategies, and improve customer intelligence. Likewise, the logistics industry also utilises the power of BI to analyse and optimise routing and scheduling, improve fleet fuel consumption, and improve many other factors essential to competitive advantage.

As mentioned above, business intelligence can be analysed using many tools. One of the most common practices is by using data visualisation. Visualising information is a great way to understand relationships and identify patterns quickly. This is because as humans, we are wired as visual creatures. According to a study conducted at the University of Minnesota, 90% of the information transmitted to the brain is visual, with human brains processing visual stimulus 60,000 times faster than text.1 Given that the healthcare industry is synonymous with processing vast amounts of information daily, BI tools such as visualisation and dashboards inherently present a myriad of benefits for the fast-paced sector.

According to a report by Stratistics MRC, the overall market share of business intelligence in healthcare is set to see an increase of about 17.4% from $3.75 billion in 2017 to $15.88 billion by 2026. Factors such as government initiatives to increase EHR (Electronic Health Record) adoption, increasing adoption of data-driven decision-making, growing pressure to reduce healthcare spending and improve patient outcomes, and big data in healthcare are some of the contributing factors to the growth of the healthcare BI market. 

Applications of Business Intelligence in Healthcare

Patient Care

Being an information-intensive industry means that healthcare professionals need to have timely, relevant, and accurate information that is easily accessible. As it stands, patient information is sourced from multiple sources, which presents a challenge when tracking patient history to deliver care. According to the journal of the Healthcare Financial Management Association, one of the primary challenges in providing quality care is the inability to manage patient data in a meaningful way.

The journal asserts that patient medical history includes data on previous medical procedures and tests, medication allergies, and prescription dosage. While this information is needed to ensure the best possible care, a physician may have access to only a portion of these critical pieces. As a result, patients are often treated episodically by providers who have access only to a limited amount of necessary clinical information.2

With BI, healthcare providers can have access to a centralised system that consolidates patient information from multiple systems. BI enables healthcare providers to access vital information such as treatment reports, biometric data, and diagnoses remotely on mobile, tablet, or web devices. BI also empowers patients to contribute to their health records through dashboards and portals accessible via mobile and wearable devices. Through patient portals, public individuals can upload images, fill out electronic forms, schedule appointments, and update other health-related data that is available on-demand to their healthcare provider. By having access to this information, healthcare providers can administer treatment plans that are tailored to individual needs, improve response rates and predictive care.

One case in which BI was used to improve patient care was when the Jersey City Medical EMS adopted a BI software called the Mobile Area Routing and Vehicle Location Information System (MARVLIS). The system utilises wireless communications and GPS and GIS technology to bring EMS to their destinations more quickly. Real-time analytics help position ambulances in areas where they are more likely to be needed. Since the adoption of the system, Jersey City EMS lowered their response rate below six minutes—3 minutes faster than the national average.

Learn how Lynx-HCF improves patient care.

 Operational Performance

While the healthcare sectors primary objective is to improve the health outcomes of individuals, the industry is no different from others in that it relies on revenues, expenses, assets, and liabilities to function. A well-known goal in healthcare is to reduce costs and eliminate waste as the industry continues to shift towards value-driven patient care.

An essential aspect of business success that BI can monitor is performance indicators (KPIs). Factors such as readmission rates, hospital infection rates, staff shortages all affect operational efficiencies.  With BI, organisations can track these metrics using interactive dashboards that provide data in real-time to allocate resources more effectively. Figures such as the number of procedures performed daily, bed occupation rates, the average duration of stay, and average patient wait time can also be analysed to improve operations.

Predictive Care

Having the ability to analyse historical data and formulate predictive responses is a significant upside to integrating BI with your organisation. Using the power of BI, predictive analytics enables healthcare providers to reduce the occurrence of health complications in high-risk patients. Improving the accuracy of diagnosis and treatment using big data also gives healthcare providers access to relevant information that uncovers unknown correlations, insights, and hidden patterns.4  As a result of these insights, healthcare providers can administer correct treatment plans to treat a specific illness.  

The NorthShore University Health System is an example of a healthcare organisation that used business intelligence tools to tackle disease. Hypertension is well-known for being difficult to identify and treat. To address this issue, NorthShore University developed a BI system that can identify patients who are at risk of hypertension. Since the adoption of the technology, the system has been used to identify, test, and diagnose more than 500 patients with previously undiagnosed hypertension.5

Another case in which BI contributed to predictive care was when the University of Pennsylvania developed a machine-learning algorithm that helped identify patients at risk for sepsis. Using EHR data from more than 160,000 patients, researchers created a tool that monitored hundreds of key variables in real-time. Alerting providers across three different hospitals, the tool identified an average of ten at-risk patients per day a full 12 hours before the onset of the illness.3

Conclusion

As the healthcare industry continues to integrate digital workflows in its efforts to be more data-driven, business intelligence presents itself as a sustainable technology in the healthcare industry that can optimise patient care and operations. The use of dashboards, reports, AI and machine learning are BI tools that are steadily becoming staples, with proven track records in improving areas such as personalised care and clinical processes. 

 

1. Vogel, D., Dickson, G., Lehman, J., Vogel, D., Dickson, G. and Lehman, J. (1986). Persuasion and the Role of Visual Presentation Support: The UM/3M Study. [online] Available at: http://misrc.umn.edu/workingpapers/fullpapers/1986/8611.pdf.

2. Figlioli, K. J. (2011). Closing the healthcare communication gap. Healthcare Financial Management: Journal of the Healthcare Financial Management Association, 65(9), 148-150.

3. HealthITAnalytics (2017). UPenn Uses Machine Learning, EHRs to Target Severe Sepsis. [online] HealthITAnalytics. Available at: https://healthitanalytics.com/news/upenn-uses-machine-learning-ehrs-to-target-severe-sepsis [Accessed 30 Sep. 2020].

4. www2.deloitte.com. (n.d.). Using predictive analytics in health care | Deloitte Insights. [online] Available at: https://www2.deloitte.com/us/en/insights/topics/analytics/predictive

5. Degaspari, J. (2013). NorthShore University Health System. Healthcare Informatics, 29(1), 12-16.

 

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