lasix without prescription Clomid without prescription celexa Levitra without prescription buy Zyvox online Diflucan no prescription cipro without prescription Cialis Professional no prescription Fluoxetine Buy Citalopram No Prescription Flomax buy jelly Cialis online Buy Doxycycline Online

  Home | Contact Us | Login | Register

Share This...Share on FacebookTweet about this on TwitterShare on Google+Share on LinkedIn

QualOptima v1.7

QualOptima Professional Practice Application Version 1.7

Exceeding Compliance For FPPE-OPPE

q_iconThe initial versions of the QualOptima clinical intelligence system focus on the traditional core competence of Q, to measure clinical performance and patient outcomes integrated with peer review tools for single case drill-down functionality. The QualOptima clinical intelligence data platform is designed to span and engage operational functions across the boundaries of quality and risk management, medical staff coordinators, and the organized Medical Staff. Version 1.7 of QualOptima integrates three applications into a single application on a common database from existing disparate data collected electronically for each all three (3) required functions of Focused & Ongoing Professional Practice Evaluation (FPPE-OPPE):

  • Version 1.3 – Clinical Performance/Outcomes Measurement (Qperform & Qreview)
  • Version 1.2 – Triggers (Qtriggers)
  • Version 1.5 – Proctoring (Qproctoring)

QualOptima v1.7 is far more than technology for compliance; but starting with these compliance requirements develops a first-to-market and unparalleled clinical database with analytics for actionable wisdom exceeding compliance to transition to value-driven performance and outcomes.


Deliverables from QualOptima v1.7

Wizards in the QualOptima clinical intelligence system are available to transfer data onto the Qualytx database to take advantage of as much existing available and useful data from previous processes and technology to initially populate the database. Some healthcare organizations will want to continue to use several compatible and useful software products from other vendors, as well as potentially integrating products from our strategic alliances. QualOptima technology also integrates data from Q’s available databases unique our customer using the machine reading and learning technology of QualOptima v1.2.

The QualOptima analytics engine displays dashboards for real-time clinical and organizational outcomes. Organizational outcomes data is both process oriented, for volume and task status data display, and results-oriented, such as:

  • Adverse Events per 1,000 Patient Days
  • Adverse Events per 100 Admissions
  • Percent of Admissions with an Adverse Event

The clinical performance and outcomes deliverable in QualOptima is an electronic process that generates both reports and data display. Q’s innovative reports formats and data displays developed from over 35+ years are illustrated in the animated example below of how data is captured electronically, analyzed and then displayed to the user.

White Papers

Innovative Features of QualOptima v1.7

The QualOptima Professional Practice Evaluation Application is a common data platform for strategic clinical intelligence systems combining all three (3) versions commercially available independently into the integrated Application including performance & outcomes measurement, triggers and proctoring.

Several product features illustrate the innovative technology of QualOptima v1.7:

  1. Evaluate clinical data for performance and outcomes measures from electronically collected and aggregated data from existing disparate databases beyond LOS (length of stay) and resource use measures which have limited validity to measure current clinical competence in granting privileges
  2. Use triggers to search for clinical indicators of sub-optimal care and machine reading and learning technologies to evaluate quality and patient safety patterns not contemplated in the triggers lexicon indicating the need for drill-down peer review
  3. Instead of asking peer review physicians to answer Quality of Care Question Sets (functionality remaining in the software application as in the graphic header), defined metrics from performance measures with defined numerators and denominators are imposed on clinical data captured electronically
  4. Integrate QualOptima technology into payment incentive and disincentive programs, such reduction of readmissions and hospital-acquired conditions (HAC’s)
  5. Use overuse/underuse/misuse analytics framework recommended by the Institute of Medicine based on a study led by Mark Chassin, M.D., now President and CEO of the Joint Commission

QualOptima v.17 Analytics

QualOptima is different for FPPE-OPPE compliance. First, by embedding precise clinical performance numerators and denominators developed by organized medical specialties, QualOptima collects clinical performance and outcomes measures. Before QualOptima, healthcare organizations relied upon antiquated process metrics like return to surgery, readmission and length of stay. Electronic data software systems have relied upon administrative data. Now with QualOptima, precise clinical data required by the metrics developed to measure value for payment based on performance, existing data from these new defined performance measures sets are electronically captured and integrated. By providing powerful analytics tools, the traditional processes of quality, risk and credentialing are transformed to achieve their optimal operational potential – optimal clinical and operational outcomes.

QualOptima is licensed with standard reports typical of the traditional reports enhanced in our clinical intelligence system and user customizable data displays. Machine reading and learning technology embedded in the QualOptima analytics engine provides actionable insight for all operational and clinical processes to achieve optimal outcomes.

Transforming Quality & Risk Management with Health Data Science Analytics

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. Machine reading with learning changes how we will identify not only case examples of sub-optimal outcomes, even with a sophisticated system of electronic triggers as in v1.2, but identifying rates of complications and electronically assessing root causes.

Integrated Peer Review with Performance & Outcomes Measurement

Peer review, which has been the traditional tool of quality management, remains anticipated by the FPPE-OPPE Standards but as a tool and not a regulated standard. QualOptima integrates the processes of measuring clinical performance from aggregated data with Q’s analytics for pattern and trend evaluation while integrating peer review for single case drill-down to assess perceptions from aggregate data and root causes for solutions. As anticipated by the Joint Commission FPPE-OPPE Standards, QualOptima functionality permits remote peer review, both to facilitate local reviews and external peer review.

Quality/Risk Management Machine Read & Learn w/ Performance Indictors

The FPPE-OPPE required system of “triggers” for ongoing performance monitoring transform antiquated manual processes and other software systems to machine read and learn with QualOptima technology tools and embedded clinical performance indicators. Q’s clinical intelligence system electronically identifies patterns and incidents evidencing a potential clinical practice trend, as well as clearly defined single incidents.

Triggers and performance criteria are used by quality for performance and outcomes measurement and evaluation, integrated with peer review drill-down processes for peer validation and root cause analysis. Risk management uses triggers for patient safety and correlation of quality evaluations and initiatives for both clinical process improvement and for human factors concerns of peer review evaluations. Medical Staff and medical staff coordinators use the results of this healthcare data analytics for presentation of information to credential members of the medical staff and for specific clinical privilege delineation.

Actionable Insight from Triggers, Machine Reading & Learning

QualOptima implements natural language processing, machine reading and learning technologies. Examples of how this technology is used to improve clinical care and patient outcomes and improve patient safety include:

  • Electronic triggers for structured (drop-down data elements) data using Q’s lexicon of clinical indicators as concept banks for triggers to identify defined single incidents or evidence of a clinical practice trend for Ongoing PPE data
  • Electronic machine reading and learning is used to identify data elements from unstructured (narrative text) in discharge summaries, history & physical examinations, operative reports, pathology and radiology reports
  • Unstructured and structured data elements are in the common data platform for analytics as clinical indicators of potential sub-optimal care to convert OPPE to FPPE
  • Clinical indicators are used to populate the peer review application of QualOptima for drill-down validation and root cause analytics
  • Electronically machine read unstructured text to identify opportunities to improve from the array of available sources such as Medical Staff Committee minutes, event reporting and patient satisfaction databases, and even the narrative text in peer review, both integrated from prior peer review and in the QualOptima v1.3 peer review and v1.5 proctoring applications
  • Electronically machine read unstructured text in risk management documentation and copies of pleadings and discovery documents to identify potential liability exposures
  • Discover actual hospital-specific clinical processes of care, evaluate against evidence-based performance measures, and evaluate effects of clinical variables in these processes with performance and outcomes focus.