Improving the Patient Experience with Cognitive Science

CMS, along with the Agency for Healthcare Research and Quality (AHRQ) developed the HCAPS survey to assess a patient’s experience and perspectives on hospital care. At a high level, the key levers included within the survey are communication, responsiveness, pain management, care transition and planning, hospital environment, and overall rating. But the challenge with the survey is that it is retrospective. By the time the hospital finds out about gaps in a patient’s experience, he or she has already been discharged.

To help providers proactively identify potential gaps in the patient experience, the Cognitive Clinical Success Machine is identifying patients at admission who have a high propensity of experiencing a gap in care. For example, the machine uses the complex Eigen Sphere topology within its Eigenspace platform to identify specific factors such as illiteracy that influence a patient’s care experience. It leverages all of the data and intelligence it has to flag these gaps in care and alert care givers to the possibility that a specific patient will have a poor experience across key levers such as communication. Providers are able to apply this intelligence to adjust the methods and resources used to support a patient such as medication labels that include pictures instead of written instructions. The ultimate goal and outcomes is to drive up patient engagement and adherence by addressing gaps in the patient experience, which translate into improved health and care quality.

Learn how hospitals across the country are using the Jvion Machine to stop patient illness, improve intervention effectiveness, and drive toward value-based models of care and reimbursement.

Engaged Patients and The Cognitive Clinical Success Machine

To determine a patient’s likelihood to engage with a specific clinical action or intervention, the Jvion Machine makes sense of the thousands of socioeconomic and behavioral factors that influence the propensity to engage.
This intelligence along with a patient’s clinical history are mapped within the machine’s Eigen-based architecture to pin point the interventions that will drive the highest levels of engagement for each individual patient. With this propensity information, clinicians can better align clinical actions and resources to the patients who are most likely to benefit and engage with specific care activities and interventions.
Learn how hospitals across the country are using the Jvion Machine to stop patient illness, improve intervention effectiveness, and drive toward value-based models of care and reimbursement.
Checking Patient Pulse
  • Bedside Patient Rescue in the Workflow

    BPR is built as a “push system” that drives assessment, follow up, and automated escalation. It is focused on enabling time-limited escalation of expertise to the bedside to stop deterioration and reduce mortality.

BPR Workflow Example...

  • Within the first 30 minutes

    An alert is sent to the RN. The Bedside Nurse signs and factor scores; Charge Nurse checks with bedside nurse as needed
  • After two hours

    An alert is sent to the Service Provider. Service provider evaluates and resolves; Bedside nurse SBAR and assist as needed. Enter vital signs and Factor score after interventions; Charge nurse checks with bedside nurse as needed.
  • After one hour

    An alert is sent to re-check patient. Service provider re-asses, broaden differential, check intervention intensity; Bedside nurse SBAR and assist as needed. Enter vital signs and Factor score; Charge nurse checks with bedside nurse as needed
  • Alert sent to Consult

    Consult broaden differential, consult team, validate goals and plan of care; Service provider communicate interventions, results and plan; Bedside nurse SBAR and assist as needed. Enter vital signs and Factor score; Charge nurse checks with bedside nurse as needed
The addition of the BPR vector to Jvion’s Cognitive Clinical Success Machine will extend our ability to stop patient deterioration and save more lives. Working in collaboration with the Mayo Clinic team, we have developed something that is positioned to help countless patients and the clinicians who care for them.

Based on an analysis performed as part of the Bedside Patient Rescue (BPR) project, failure to recognize acute patient deterioration is the most common contributor to mortality*. But current early warning scores that combine expert opinion and classical statistical models are not very effective in lowering mortality rates.

Hospital Acquired Conditions Overview

  • Hospital Acquired Conditions (HACs) are conditions that are defined by section 5001(c) of the Deficit Reduction Act of 2005 that:

    • Are high cost, high volume or both,
    • Result in the assignment of a case to a DRG that has a higher payment when present as a secondary diagnosis, and
    • Could reasonably have been prevented through the application of evidence‑based guidelines.
  • catheters
These common medical errors add up to more than $4.5 billion in additional spending each year.

HAC Scores

HAC scores are based on six quality measures that fall into two domains.
Domain 1: Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator (PSI) measure 90 Composite
Domain 2: Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) Healthcare-Associated Infection (HAI) measures including:
  • Central Line-Associated Bloodstream Infection (CLABSI)
  • Catheter-Associated Urinary Tract Infection (CAUTI)
  • Surgical Site Infection (SSI) (colon and hysterectomy)
  • Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia
  • Clostridium difficile Infection (CDI)

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References

Healthcare Associated Infections Overview

  • Healthcare-Associated Infections (HAIs) are cited by the World Health Organization (WHO) as a critical public health problem because of the impact that HAIs have on morbidity and mortality around the world. The most common global HAIs include infections of surgical wounds, the blood stream, the urinary tract, and the lower respiratory tract.
  • surgical-equipment

In addition to the most common infections, diseases including severe acute respiratory syndrome (SARS), viral hemorrhagic fevers such as Ebola, avian influenza, and pandemic influenza have placed special focus on the ability of providers to stop the spread of outbreaks within the facility and out into the community. Adding to the complexity is the emergence of antibiotic resistance that has created "super-bugs," which are difficult to treat and manage. This is critically important for hospitals that may act as "permanent reservoirs" of resistant bacteria.

  • Within the United States, HAIs are estimated to impact close to 1M people per year according to a 2011 Centers for Disease Control and Prevention (CDC) report.
  • The infections break down to:

    • Pneumonia: 157,500
    • Gastrointestinal Illness: 123,100
    • Urinary Tract Infections: 93,300
    • Primary Blood Stream Infections: 71,900
    • Surgical Site Infections: 157,500
    • Other Types of Infections: 118,500

A recent CDC study found that on any given day, approximately 1 in 25 hospital patients has an HAI. Moreover, about 75,000 patients with an HAI died while in the hospital.
In 2008, the Federal Steering Committee for the Prevention of Health Care-Associated Infections was established.
This group includes individuals from the Department of Health and Human Services, U.S. Department of Defense, U.S. Department of Labor, and U.S. Department of Veterans Affairs. This group developed The National Action Plan to Prevent Health Care-Associated Infections: Road Map to Elimination in 2009. This plan provides a framework and direction on how to eliminate HAIs within acute care settings, outpatient settings, and long-term care facilities. Additionally, the Partnership for Patients program was established under the Centers for Medicare & Medicaid Services (CMS). This program is aimed at driving public-private partnerships to reduce HAIs and readmissions. Twenty-six organizations are working with more than 3,700 hospitals to meet target reduction levels over the next three years.

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References

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