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Optimal Outcomes Analytics

q_iconThe imperative to improve performance and achieve optimal clinical, operational, and financial outcomes is the new challenge to healthcare decision makers. Their urgent need is to harness the power of strategic analytics and big data, not from the failed products relying on claims and billing data but with structured and unstructured clinical data integrated with financial data from the myriad of existing disparate databases available in every health care organization. Efficiencies and effectiveness in care and organizational operations, anticipating patient-generated data from engaged patients and personalized/individualized care, are the essence of QualOptima’s analytics to achieve optimal clinical, financial and operational outcomes to compete in the evolving healthcare environment. Value-driven results demand collaboration between management, physicians, clinicians, nurses and their patients, payers and insurers as never before and in an ever increasingly competitive and regulated market. QualOptima’s clinical intelligence system empowers data-driven decisions to achieve results-driven success..


QualOptima Strategic Path to Big Data and Value-Driven Care


This powerful, highly-scalable common data platform is available to all users in the healthcare enterprise for strategic analytics based upon QualOptima’s system of personal risk and fitness factors aligned with evidence-based performance metrics using clinical data (not only billing codes) from disparate EMR and vendor systems with Q’s embedded clinical content, metrics and analytics empowers providers and payers to achieve optimal clinical, operational and financial outcomes. Q’s customers are engaged in a value-driven data plan to transform their enterprises with a combination of strategic data analytics and “big data”. Analyzing large quantities of data (high volume) with data variety at high velocity is called “big data,” with the three (3) characteristic “3 V’s” of volume, variety, and velocity. The QualOptima common data platform is a large collection of disparate data sets (inevitably from disparate vendor databases integrated with the electronic medical record database) that are analyzed for learning and pattern recognition resulting in actionable wisdom.The path to value-based care from healthcare data science and transformation with digital health begins by learning from integrating data and learning for actionable knowledge from strategic analytics to transform outcomes. Optimal patient management and quality must be demonstrated by outcomes correlated with resource use and cost. The quality-value relationship is the fabric of QualOptima.
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Transform Quality & Risk Management and Credentialing


Benefits of adoption of the QualOptima Connectivity & Analytics System begin with improving quality and patient safety by exceeding the compliance requirements of the Joint Commission for Focused & Ongoing Professional Practice Evaluation. QualOptima v1.7 is fundamental to implementing clinical performance and outcomes measurement with credentialing focused on optimal clinical processes.
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QualOptima v1 tools are designed to comply with the essential requirements of performance and outcomes measurement, triggers to identify potentially suboptimal outcomes and clinical processes, and to proctor clinical practitioners to assure current clinical competence. The initial product is designed to exceed the myriad of requirements for:

  • Compliance with the strategic imperatives to improve clinical outcomes and implement the requirements of the Joint Commission for Focused and Ongoing Professional Practice Evaluation (FPPE-OPPE).
  • Compliance with strategic imperatives to transition to new payment systems driven by value and penalizing suboptimal outcomes while incorporating the potential of meaningful use of electronic medical records and new provider entities consistent with these imperatives.

QualOptima v1 is intended as the transformative technology to transition from antiquated quality, risk and credentialing systems to the opportunities from new data science with strategic analytics and big data. QualOptima v2 is the common data platform crossing the traditional boundaries and silos of quality, risk and credentialing for the new frontier of healthcare quality science.

Software technology has relied upon systems that look for specific defined indicators of potentially suboptimal care, whether called “clinical indicators” in peer review processes or now “triggers” in FPPE-OPPE standards. This is similar to the typical scientific method of establishing a hypothesis and then collecting data to prove or disprove that hypothesis. Big data provides the technology to learn from large data sets rapidly from a variety of data. Machine learning with advanced analytics finds causal relationships to identify classes of adverse outcomes and events such as prediction and signal detection. New big data methods can turbocharge powers of observation in healthcare. In the same way the microscope enhanced eyesight, sophisticated mathematical and computational approaches can augment what can be “seen” and understood from massive amounts of data.

Data-driven technology to illuminate suboptimal outcomes and patient safety concerns from diagnostic and treatment modalities, drugs and medical devices will first be identified and then analyzed for root causes. The new data science will not only identify suboptimal outcomes but will scrutinize optimal outcomes to transfer clinical knowledge to improve outcomes in other processes of the enterprise.

QualOptima is the large data platform integrating the total array of available disparate data with the technology to implement big data and improve outcomes while reducing costs through optimal clinical processes. As a series of articles in a recent Health Affairs journal discuss, when a patient has an adverse event, which of the many dimensions of data are important – and which can be ignored. By capturing, integrating, aggregating and analyzing exponentially growing sources of data, healthcare quality, risk and credentialing processes will transform the enterprise of health care to achieve optimal outcomes.
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Become Learning Organizations Empowered by Strategic Analytics & Big Data


The urgent opportunity confronting medicine, health care, insurance and health/wellness is to adopt methods and technologies to become true learning organizations. QualOptima was developed anticipating this challenge with powerful technology to rapidly advance health care with information science and strengthen the foundations of continuous quality improvement through learning organizations. Advocated since 2007 by the Institute of Medicine (IOM), such a system is defined as one “designed to generate and apply the best evidence for the collaborative healthcare choices of each patient and provider; to drive the process of discovery as a natural outgrowth of patient care; and to ensure innovation, quality, safety, and value in health care.”

Collaborative teams in hospitals and among physicians, payers and data scientists will transform health care both with new delivery and payment models. Data science relies upon elements of signal processing, statistics, machine learning, text retrieval and natural language processing. Translating new evidence from experiential evidence into routine practice, particularly in health care which has been notoriously slow to adopt innovation will become an important challenge in medicine. The evidence and the scientific method must be sound for physicians, regulators and payers to have confidence in new delivery and payment models. The essence of a truly learning health care organization will be to learn continuously from daily experience.


Improved clinical process for improved outcomes


Optimal clinical outcomes are achieved and continuously improved through optimal clinical processes. QualOptima’s proprietary design is based on a system of gaining actionable insight from analytics based on measuring evidence-based clinical performance. All patients are not the same, and clinical processes should be implemented based on personal risk factors specific to the patient for patient-centered care.

Q’s Clinical Trial of QualOptima at the University of Miami, Jackson Memorial Hospital, demonstrates the power of its clinical intelligence system. For example, patient-specific clinical care data on the use of antiemetic drugs to prevent post-operative nausea and vomiting (PONV) is a clinical process. By analysis of patient-risk factors and the use of antiemetic drugs, choice of drug, sequencing, dosages, and salvage protocols reveals significant actionable wisdom for anesthesiologists in their clinical processes of care. Analytics provide unique insight based on patient-specific outcomes of PONV.

QualOptima personalized/individualized clinical processes data and analytics will become even more valuable when measured across the continuum of care, not only for perioperative clinical processes but pre-operative, in-patient post-operative and discharge/transitions of care including ambulatory care. When supplemented by QualOptima’s versions in development with strategic alliances, patient satisfaction techniques will be replaced by new patient to physician communication systems to optimally manage post-op care with mobile apps, for example.

Predictive modeling will become a significant benefit of QualOptima technology. For example, patients with body temperature maintenance or PONV complications will electronically alert for a high-risk protocol after leaving recovery. True learning organizations will use QualOptima analytics from machine learning tools to read medical records to identify clinical processes by department or service, practitioner-specific, to determine clinical variables and opportunities to learn from both optimal and sub-optimal outcomes.


Managing high-risk high-cost patient populations


Population health has become a significant emphasis in health care for specific patient clinical conditions. In the United States, approximately 5% of patients account for about 50% of all health care expenditures. Managing these identified patient groups more effectively and efficiently is one significant approach to improving care while reducing costs. Analytic methods to identify patient groups (and what will become the “phenotypes” of sub-groups within these conditions), predicting which patients will become high-risk or high-cost, and identifying specific interventions to most improve care is central to QualOptima functionality.

Specific patient conditions can be effectively improved in ambulatory settings with behavior modification using the Internet, which has been demonstrated and will result in changing medical delivery and payment models. QualOptima as the Connectivity and Analytics System becomes even more central to these strategies. The complete solution of QualOptima will become evident when the potential of patient-generated data, remote monitoring devices, and payment for online physician consultation is fully achieved.