Data and analytics are the foundation of healthcare, supporting everything from patient diagnosis, decision support, and episode of care management to quality monitoring and population health improvement initiatives. Analytics in healthcare today involves analysis of EHR and related digitally available information about an individual, a patient, or cohort of patients, and applying analytical tools to support the administrative, financial, and clinical goals of a health services organization.
With the proliferation of digitized healthcare data pouring from all corners of the ecosystem, modern health analytics has become a demanding and complex discipline that requires advanced systems and skills. Transferring patient data to a healthcare cloud that provides health information interoperability, scalable compute power, and complimentary analytics tools allow users to support predictive analytics functions that vastly expand the power of data to support organization goals.
Enhancing the efficacy of patient care is the driving force for many health analytics initiatives. Data has become a valuable tool to leverage when setting care plans or organization objects with the goal of envisioning possible diagnosis or treatment outcomes. These predictive analytics can create comprehensive and real-time guidance for healthcare professionals and clinicians enabling them to draw reasoned conclusions and make more informed decisions. In a clinical setting, predictive analytics can facilitate quicker and more accurate diagnostics, in turn improving patient care and experience.
How can healthcare organizations best take advantage of this digital proliferation, expand their organization’s culture to be more data-driven, and enhance care via predictive analytics? First and foremost, let your organization’s mission drive strategic priorities and focus on improved patient outcomes. By getting control over data across health systems, including varied medical record systems and other related sources, organizations can greatly improve their ability to identify warning indicators of significant medical events and proactively avert negative incidents.
In addition, a data-driven culture with predictive analytics embedded into workflow advances patient focus on the whole person, rather than on individual test results or diagnosis. Predictive model approaches help demonstrate the value of collecting and integrating multiple data points, across multiple platforms to construct a holistic treatment plan of action. This idea also plays into a more personalized patient service model. Using predictive analytics, healthcare organizations can personalize treatment plans to unique patient needs that are more closely aligned with their ability to engage in their care plans and lead to stronger, more positive outcomes.
Utilizing predictive analytics across the healthcare enterprise can enhance operations efficiency, diagnosis and treatment of individuals, and management of population cohorts at risk.
At Quantiphi, through our work with clients and our own research, we continually look for areas where predictive analytics can make a positive impact in healthcare systems. This is reflected in our offerings and the projects, across settings, that we’re engaged with clients to address challenges that they face every day. Some relevant examples are highlighted below.
Disease Recurrence Prediction: In order to assist a US-based large healthcare system with efficiently flagging cancer recurrence, Quantiphi devised an intelligent solution that included a comprehensive and custom ML model to reliably predict recurrence in oncology patients as relevant information occurred in their network.
The predictive classification model enabled tagging patient encounters as related or unrelated to cancer recurrence for oncology patients. Included in this model were both structured and unstructured Electronic Health Record (EHR) data such as the patients’ medical profiles, physician notes, pathology reports, lab reports, etc. to flag the patient feature associated with tagging the encounter as a recurrence. This enabled physicians to proactively deliver life-saving assistance and provide targeted treatment to high-risk cancer patients.
Care Management Efficacy: Quantiphi recently empowered a leading healthcare organization to harness the power of historical and real-time data using a combination of AI and predictive analytics to measure the efficacy and value of care programs interventions aimed to maintain optimal patient health and decrease healthcare expenditure.
The AI-powered care management solution analyzes the effectiveness of patient care programs by utilizing disparate sources of data. It leverages predictive analytics to deduce the propensity of care acceptance by a patient. Furthermore, the solution aggregates clinical, administrative, and customer data to create a holistic 360° patient view for stronger insights and improved individual care. The solution helped the client efficiently monitor the performance of care plans and deeply transform healthcare by tailoring care precisely to the individual.
Patient No-Show Predictive Model: Patient no-show is one of the biggest obstacles for providers and clinical organizations impacting resource planning. Quantiphi introduced a powerful and high accuracy solution that ingests information from multiple data sources, processes this information through an ML model, which ultimately enables clinical organizations to identify patients with a high probability of not showing up to their scheduled appointment. By implementing this solution, Delta Dental was able to generate and view predictions in real-time, reduce manual effort and time, and gained high-cost savings.
Boost Operational Efficiency with Predictive Analytics: Healthcare providers make a multitude of operational decisions in exceedingly high volume every day. To run a smooth, productive organization for patients and medical staff requires sophisticated data science. The rising demand to become more streamlined, cost-effective and do more with less is driving the adoption of predictive analysis among large-scale hospitals today.
In other words, predictive analysis helps healthcare organizations to harness already existing data into a strategic, cohesive patient experience that streamlines operations while improving patient outcomes.
Data analytics will increasingly be used by healthcare organizations to predict the probability of future scenarios and make better decisions. This will enable personalized patient care and treatment, as well as improved operational efficiency of healthcare systems.
Data fuels predictive analytics and access to data at the right time is essential for predictive analytics to be effective. Quantiphi leverages data analytics and AI to help healthcare providers improve outcomes and reduce costs.
According to the research published in the Journal of the American Medical Informatics Association, “the combination of a computerized surveillance algorithm and clinical decision support (CDS) tools amounted to 53 percent reduction in deaths per 1000 cases in health systems as compared to the control group (i.e., from 90 to 40 deaths per 1000 cases). When studying sepsis identification more widely using IDC-9 codes, the former figure dropped to a still significant 41 percent lower mortality.” Leveraging data and predictive analytics to avoid complications and improve care planning will make healthcare safer.
As data access and management across healthcare ecosystems continues to mature predictive analytics will be employed widely to assess potential future conditions, reduce the risk of care complications, and improve quality. At an organizational level, data and predictive analytics will drive more efficient healthcare operations, optimize asset utilization, and reduce costs.
By automating processes and providing integrated solutions, we further our commitment to seamless care and better patient outcomes. With our partners, we work to support the integration of the healthcare systems by providing innovative connected technology.
Get in touch with our experts to integrate predictive analytics into your workflows.