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University Of Miami Clinical Trial

University Of Miami QualOptima Clinical Trial

The Challenge of Clinical Performance & Outcomes Measurement

q_iconQuture (“Q”) was formed to transform its peer review processes to measure clinical performance and outcomes for health data science analytics using the most advanced strategic technology platform in the world. Technology to accomplish Q’s vision relies upon the most powerful integration engine available to capture existing clinical data and integrate it into the Qualytx database for advance analytics and big data to transform information to knowledge solutions. Q’s Application Partnership Agreement with InterSystems required a proof of concept and statement of work to demonstrate implementation of the Ensemble interface engine for data capture and integration into the QualOptima database structure, developed in Cache.

fatherson_thumbMeasuring clinical performance has become a major emphasis in health care to improve quality and reduce costs, in the United States and internationally. The Institute of Medicine introduced a framework and implementation strategy to translate professional and public concerns about performance and accountability into measures of health care quality in the Pathways to Quality Health Care series of reports commencing with Performance Measurement: Accelerating Improvement in 2006. The recent history of performance measurement in relation to the imperative to improve quality of care and reduce costs is summarized as follows.

After the release of the Institute of Medicine (IOM) report To Err is Human, improving quality of health care in the United States (US) has become a national priority. There is also a growing realization that the dramatic growth in health care costs is not economically sustainable. Total spending on health care is projected to increase to nearly 40% of the gross domestic product by 2050. The imperative to improve quality and cut costs has led the federal government to initiate public reporting with Hospital Compare, and to incentivize physician and hospital performance using pay-for-performance. The Centers for Medicare and Medicaid Services (CMS), along with key stakeholders such as the Joint Commission, the American Medical Association, the Physician Consortium for Performance Improvement (PCPI), and many medical societies such as the American Society of Anesthesiologists (ASA), are developing performance measures for hospitals and physicians.

Significant publications in the medical literature describe performance measurement as the “transformative tool,” widely recognized as the cornerstone in the drive to improve health care quality. Performance measurement will be used to transform hospitals into ‘learning laboratories’ to discover and implement best practices.
Numerous healthcare related organizations are currently focusing their attention on measuring outcomes and are promoting strategies to improve the quality of healthcare. Initiated by the CMS, value-based purchasing based on payment for performance and outcomes, now sometimes called “fee-for-value”, is one such strategy: it reimburses hospitals and healthcare providers based on efficiency and encourages quality improvement by reducing potentially preventable events. For this strategy to be successful, it is essential to determine which outcomes are important to have in place as accurate method for capturing outcomes data.

The key role of performance and outcomes measurement is now being adopted in anesthesiology to improve quality with the creation of the Anesthesia Quality Institute (AQI) and the National Anesthesia Clinical Outcomes Registry (NACOR). The ASA is creating the data infrastructure for quality benchmarking and outcomes improvement in anesthesiology. The significance of quality reporting is now transitioning to payment incentive programs legislated by Congress in the 2006 Tax Relief and Health Care Act and the value-based purchasing payment system by fee-for-value legislated in the Affordable Care Act (2010). Performance assessment is now a core component of anesthesiologist credentialing through the Board of Anesthesia Maintenance of Certification and the Joint Commissions Standards.

Success Story

The QualOptima first-ever transfer of electronic performance and outcomes data personalized to the patient’s risk factors marked the opening of a new era in performance and outcomes measurement, analytics, patient safety, and quality with data-driven clinical process improvement.

Q retained Keith A. Candiotti, M.D., Executive Vice-Chair & Chief Operating Officer and Vice Chair for Clinical Research, and Chief, Division Perioperative Medicine as well as Professor of Clinical Anesthesiology, Department of Anesthesiology, University of Miami Miller School of Medicine, as our Associate Medical Director for the QualOptima product. Dr. Candiotti led his team of anesthesiologists, research and health data scientists, information technology experts through design, development and testing in a formal Clinical Trial supervised by the Investigational Review Board. The extensive intellectual property of Quture, embedded in this design and development, was guided by Dr. Candiotti and his team working with Q’s technology strategic alliance with Q’Zure, LLC under the leadership of Sherif Elfayoumy, Ph.D. This demonstration of the InterSystems technology was coordinated with their software engineers assigned to this Clinical Trial.

Solutions for Collecting & Integrating Clinical Data from Disparate Databases

Dr. Candiotti selected measurement of evidence-based optimal clinical processes and metrics based on national acceptance. The Protocol approved by the IRB, entitled “Electronic Performance and Outcomes Measurement in Anesthesiology: Demonstration of the Potential Value of Analytics Based on a Scientific Data Model Utilizing a Unique Technology Platform,” selected two (2) evidence-based measure sets. One of the three (3) formally adopted measure sets of the ASA was selected as metrics maintaining body temperature perioperatively The second measure set to reduce post-operative nausea and vomiting (PONV) was selected based on an evidence-based best practices clinical guideline of the ASA and two (2) other professional anesthesia organizations. This first-ever transfer of electronic performance and outcomes data personalized to the patient’s risk factors marked the opening of a new era in performance and outcomes measurement, analytics, patient safety, and quality with clinical process improvement. of one measure by thto reduce post-operative nausea and vomiting (PONV) and maintaining body temperature perioperatively.

The QualOptima interface engine successfully captured clinical data from two (2) different vendor databases, including the patient electronic record and the perioperative electronic record. The collected clinical data was integrated successfully into QualOptima’s Qualytx database for analytics. The database structure design for Qualytx was then engineered and programmed for integration with Q’s peer review database to unite these processes into data aggregation for pattern detection and use of peer review to drill-down to root cause analysis.

Solution to Embed Q Intellectual Property in Qualytx Database

Q’s intellectual property (IP) includes a medical care model with corresponding algorithms for analytics. QualOptima connects individual patient risk factors with evidence-based perioperative performance measures compared to patient outcomes. Q’s embedded data structure is its DaTA© construct to capture patient-specific risk factors, performance and outcomes metrics, for analytics with clinical variables.
QualOptima was proven to provide data and analytics to impact compliance with evidence-based optimal clinical processes on outcomes and costs. Costs associated with extended stays and complications in recovery and beyond for well-established performance measures in anesthesia connects clinical quality with outcomes and costs.

QualOptima incorporates the analytics tools of the InterSystems DeepSee product, which provided the working laboratory to program Q’s algorithms and integrate existing technology into product development.

Solution to Develop Predictive Modeling from Performance & Outcomes Data

The potential of the product is being evaluated for predictive modeling and developing clinical decision support systems, as well. The success story of QualOptima at the University of Miami was proven for predictive analytics. When significant outcomes of PONV and body temperature deviation are known, with increased risks of complications, the patient can be placed on specific protocols for post-operative management to reduce complications and potential hospital readmissions.