Analytics for Healthcare
In recent years, organizations such as Amazon have thrust customer experience into the limelight as a key competitive differentiator leading to heightened consumers expectations from all sectors and industries – including healthcare. Electronic Health Records and the explosion of data available to Healthcare providers presents a compelling opportunity to create value from volume. Through the use of advanced analytics, machine learning and technology, organizations can reduce costs, drive better outcomes, provide more with less, ultimately, moving to a patient-centric value-based model of Healthcare.
The Analytics for Healthcare Focus Day will establish a platform of discourse for analytical-driven leaders within this sector to share case studies, best practice and benchmark capabilities.
In recent years, the acquisition of data from health systems and electronic health records has resulted in a cumbersome amount of information. This data has the power to transform healthcare informatics. However, much of this potential value will not be realized unless liberated from disparate silos with rigorous analytics employed to learn and feed that… Read more.Speaking
Keynote Presentation: Implementing a strategic imperative for analytics within the healthcare enterprise
Developing the roadmap for the employment of advanced analytics to revolutionize the traditional healthcare enterprise with a modern approach to informatics. Assessing the needs of today’s healthcare environment and the case for an integrated analytics approach to reduce risk and optimize health outcomes, financials, service and value-based care. Leveraging analytics to shift from reactive to… Read more.Speaking
Which stories can aid you in gaining physicians support for analytics objectives? How do you enable the whole organization to see the role they play in data collection through to innovation? Case studies of experience.Speaking
Is your current analytics infrastructure keeping you from success in healthcare analytics? What are the key considerations needed to develop enterprise infrastructure to support scalable analytics and data visualization? Highlighting the opportunities for data linkage including clinical and claims data to improve patient outcomes and operational efficiency.Speaking
Creating a culture of accountability and the deployment of analytics in support of clinical decisions at every level of the organization. Assessing the training implications when adopting an insights-driven culture within a healthcare context. Developing analytics as a service, and aligning the Data Scientists to leverage social, claims and EHR data to empower organizational functions.Speaking
The transition from volume to value and enhancing patient experience by delivering scalable actionable insights is no mean feat. How can healthcare executives re-imagine processes through an agile architecture that uses analytics, technology and organizational design? How do healthcare providers build a value-based health care system of the future? What are the considerations needed?Speaking
Current state of machine learning capabilities and use cases for improving diagnostics, predicting outcomes, and personalized care. Defining the vision forward for AI and Machine learning capability in Healthcare. Evaluating implications on traditional processes and building the architectures today to capitalize on disruptive innovation brought on by the acceleration of AI and machine learning technology.Speaking
With patient data being ever more pervasive than ever, how do you move towards the ubiquitous use of analytics and value extraction? Enhancing your structured data offering by combining it with data aggregation from unstructured disparate silos to build a more complete view of patient’s health. From initiative to value! Communicating the importance of insights… Read more.Speaking
Defining clinical analytics – understanding the capabilities and usefulness of insights in the clinical development program. Developing metrics for big data analysis in clinical trials – handling industry best practice and company specific metrics. Mining different layers of data – the challenges of clinical data analysis and visualization for stakeholders. Harmonizing data – how to… Read more.Speaking
Improving predictive analytics understanding within your organization to create an agile response to insights extracted. How do you leverage insights from predictive models to treat and prevent chronic disease and help make that more transparent for the physician community? Integrating predictive analytics into the workflow to maximize service delivery.Speaking
Highlight the challenges fraught with transitioning to a preventative paradigm with increased focus on positive health outcomes. Best practices in aggregating clinical and claims data for segmentation and minimizing costs from treating chronically illness. What are patients doing when not in the hospital? Understanding behavioral patterns externally to the hospital and intrinsic connection to the… Read more.Speaking
Keynote Presentation: Case study – Integrating predictive analytics for patient care and wait time calculation
Assessing the importance of patient wait-time management and predictability can hardly be overestimated. Lessons learned in predicting wait-time and impact on hospital efficiency; including reduced patient queues, greater accuracy in data capture and heightened workflow. Use cases for predictive analytics and machine learning including risk calculation going beyond readmission’s probability metrics to successfully determine the… Read more.Speaking
Define the initial steps needed to begin the journey of digital transformation in healthcare and what this means for quality of care and empowering practitioners to allow patients to participate in their own healthcare management. Embedding the benefits of next-generation healthcare digital patient experience into your analytics strategy. Identify opportunities for greater connectivity within the… Read more.
Analysis of Machine Learning capabilities and how it’s being used to disrupt predicting chronic illness to using healthcare analytics to reduce hospital readmission’s. Case studies on the deployment of machine learning in healthcare environments. Breaking down silos and evangelizing technological education throughout the whole enterprise to foster innovation.Speaking
Associate Chief Medical Informatics Officer & Assistant Professor - UT Southwestern Medical Center