How Healthcare Analytics Improve Patient Care and Operational Efficiency

New technologies and the wide adoption of automation tools are transforming the healthcare industry. But while medical organizations already know how to aggregate lots of health data, many of them still struggle with their inability to put the collected information to meaningful use. The implementation of healthcare data analytics is a proven way to resolve this challenge. 

Like in any other industry, data analytics in healthcare enable more efficient, fact-based decision-making and accurate predictions. In this article, we’ll discuss the value of healthcare analytics for medical organizations in more detail and describe an example of the analytics adoption model. 

Benefits of data analytics for healthcare organizations

As mentioned, just gathering the information isn’t enough for a medical organization to produce noticeable results. Data brings real value only if it’s properly analyzed and insights are delivered to the right people at the right time. Let’s see what benefits a hospital can expect after it has applied health analytics to the collected information.

Improved patient care

The 2020 Redpoint survey shows that 54% of patients believe that their healthcare recommendations could be more personalized if a provider or insurer had access to all the necessary contextual information. However, the problem is that various patient care systems generate fragmented data that don’t provide a comprehensive picture of the person’s health conditions. It’s like having hundreds of pieces of a puzzle in dozens of different boxes —theoretically, you can put them together, but that would require long hours of manual work and a huge effort. 

Healthcare data analytics can combine all fragmented data points into a single patient record recognizable across all systems shared between different providers and insurers. With a unified patient profile that automatically analyzes medical records, claim history, drug intake, and possible risks, providers can perform a more comprehensive patient analysis and offer more personalized care. Having all information easily accessible will also help doctors improve preventive care and disease management while reducing the risk of medical error.  

Faster at-risk patient identification

Data analytics in healthcare can also reduce hospitalization and readmission rates by predicting possible health risks long before the illness progresses and becomes a real problem. Besides improving treatment outcomes, the timely risk assessment helps providers better coordinate care and optimize staff allocation. 

For example, the emergency department of ​​the NorthShore University Health System uses predictive analytics to determine which patients with chest pain have to be admitted for observation and which patients can be safely sent home. This helps them avoid unnecessary hospitalizations, resulting in numerous benefits: shorter wait times, more vacant beds, less work for medical personnel, etc. Another case is the Mayo Clinic. It is working on two AI-based solutions that will detect “silent” conditions (e.g., ​​arrhythmias and weak heart pumps) early on and integrate insights from remote telemetry devices with clinical workflows.

The power of hospital data analytics became especially apparent in the context of the pandemic. For instance, at Quantum, we designed the AI-powered decision support system that assists medical staff in monitoring COVID-19 patients. Based on the collected data, the system is able to suggest the next steps according to the treatment protocol and provide recommendations on whether hospitalization is needed.

Advanced diagnostics

Healthcare analytics can put patient data to more efficient use, both on the level of a specific individual task and in the context of continuous care. Healthcare organizations usually need very specific solutions to determine a particular type of disease or drug effect. AI technologies such as natural language processing (NLP), machine learning, or computer vision can be of great help when identifying and classifying various health-related conditions and symptoms.

At Quantum, we have developed several AI-powered systems that improve patient diagnostics in specific areas: 

• An NLP-powered tool for identifying connections between diseases and genetic mutations. This tool scans data from the PubMed library and then matches the findings with patient data. The system automates a significant part of the analytical work that is traditionally done manually, making the diagnosis process quicker and more accurate.
• A solution for blood cancer classification. This healthcare analytics tool determines the type of cancer by automatically analyzing separate pieces of data such as the patient’s history, demographics, as well as CBC and lab test results. The solution processes a lot of information in a matter of minutes, relieving care providers of the task of manual examination.
• A solution for drug-resistant epilepsy (DRE) prediction. This system is built for a multinational biopharmaceutical company. It identifies DRE based on the patient data that the client had been accumulating for 5-10 years. To achieve this, we implemented deep learning algorithms that learn from raw data and continue learning from their own observations, making more accurate predictions over time.

In general, technology-driven analytics in healthcare exceed human capabilities in the quantity of processed information and the accuracy of analytical results. This makes it especially beneficial in most situations when a wealth of data needs to be analyzed for a diagnosis.

Increased cost-efficiency

Medical organizations often implement healthcare analytics for performance improvement and cost reduction. While enhancing patient care and outcomes remains a top priority, cutting operational expenses is usually one of the essential objectives of adopting analytical tools. By expanding the ability to produce and share hospital data insights, these tools can actually drive revenue growth. 

For example, hospital operations can become more cost-efficient with data analytics software that detects the gaps in the revenue cycle and forecasts the issues that might affect it. Also, by analyzing payers’ behavior, medical organizations can predict and better manage denials, as well as determine how long it takes for each specific claim to be paid.

In addition, the insights gained from the implementation of business analytics in healthcare processes can lead to improved allocation of resources. For instance, analytical tools can anticipate equipment demand or evaluate the effectiveness of treatment programs. All these naturally lead to a reduction in operational costs.

The stages of a healthcare analytics adoption model

The MIT Sloan Management Review identified three stages of analytics capability progression in healthcare: aspirational, experienced, and transformed. It shows that the adoption of analytical tools begins with a limited number of users with a limited range of usage and moves progressively toward being an established cultural norm. 

In 2019, Health Catalyst and Healthcare Information and Management Systems Society (HIMSS) designed a more detailed 9-step healthcare analytics adoption model. It represents all the stages that an organization has to go through to get to the point of value-based, data-driven care. Let’s take a closer look at them.

• Level 0. A zero-stage analytics adoption in healthcare is characterized by the complete fragmentation of data points and inconsistent usage of patient information. With no analytical tools in place, medical organizations suffer from data overlaps that create poorly managed multiple versions of the truth. At this stage, the staff is burdened with a significant number of manual data aggregation and reporting tasks.
• Level 1. Level one revolves around putting different data points together and creating a solid enterprise-level foundation for data management. A data warehouse is established and regularly updated: it can include information from EMRs, supply chain management, financial data, and insurance claim details.
• Level 2. At this level, organizations put together a standardized vocabulary to establish consistency with namings and data types. Patient registries have also evolved to become standardized.
• Level 3. The next stage of analytics adoption automates the processes of internal reporting. Thanks to efficient automated reporting production, key performance metrics are unified and transparent, becoming accessible at any level.
• Level 4. The healthcare industry is reliant on many third-party players: it is heavily regulated and has different cross-organizational incentives and programs that hospitals and insurers are subject to. After handling internal reporting in a more efficient manner, it’s time to automate external reporting and adhere to industry standards related to regulatory requirements, payer incentives, specialty society databases, etc.
• Level 5. When a healthcare organization has learned to unify and automate the usage of data, it can move toward the advanced measurement of data. At this point, the major focus of healthcare analytics is to improve the quality of care by using the combination of evidence-based, standardized data taken from clinical observations, labs, pharmacies, insurance claims, and other sources.
• Level 6. At this stage, healthcare providers can better manage and evaluate both the quality and cost of care. Their analytical systems help assess financial risks and rewards based on predictable clinical outcomes, and compensation plans are based on the quality of provided care.
• Level 7. When analytics have already established a connection between the usage of data and the revenue cycle, healthcare organizations can move to even more efficient reimbursement models where all parties involved—from patients to hospital workers to insurers—collaborate to assess and distribute financial rewards.
• Level 8. This level is characterized by high personalization and allows healthcare providers to expand to general wellness management, offering prescriptive analytics at points of care. The aggregated patient record is fuller than ever and includes biometrics data, while the overall data system is updated every few minutes.
• Level 9. The last stage represents a desired state of healthcare that expands to direct-to-patient analytics—the highest level of treatment personalization and a collaborative decision-making environment. AI-based tools play a crucial role here, recognizing patterns in patient behavior and improving the precision of medical decisions.

The majority of medical executives acknowledge the importance of data collection in healthcare and are ready to adopt healthcare analytics for quality and performance improvement. However, most organizations have yet to grow in analytics maturity. The adoption model represents the only way to progress, with each new level bringing significant transformations to both providers and patients.

Conclusion

The medical sector today is facing a great dilemma—having too much data but failing to produce insights from it. To resolve it, the next step for the industry should be the adoption of powerful healthcare analytics and enabling more data-driven decisions. Hospital data insights delivered at the right time and context can lead to improved clinical outcomes and operational efficiency, which will bring broader advantages, such as increased patient and doctor satisfaction and higher revenues.

Many healthcare analytics tools already solve common problems in the medical industry by analyzing huge amounts of data in real time. But what we are yet to see is the development of analytics maturity in healthcare organizations that will drive value-based care even further.