{"id":8498,"date":"2018-11-06T14:33:01","date_gmt":"2018-11-06T14:33:01","guid":{"rendered":"http:\/\/bernoullihealth.com\/?p=8498"},"modified":"2018-11-06T14:33:01","modified_gmt":"2018-11-06T14:33:01","slug":"the-roi-of-continuous-surveillance","status":"publish","type":"post","link":"https:\/\/www.light4soul.com\/testingsite\/cardiop\/the-roi-of-continuous-surveillance\/","title":{"rendered":"The ROI of Continuous Surveillance"},"content":{"rendered":"

\"The<\/p>\n<\/div><\/section><\/div>\n

The ROI of Continuous Surveillance<\/span><\/h1>\n

by\u00a0Brian McAlpine,
\nVice President Strategy and Business Development<\/p>\n<\/div><\/section>
\n

<\/span><\/span><\/div><\/p><\/div>\n

The successful implementation of real-time patient safety initiatives has long been a goal of health system leaders. Unfortunately, parsing alarms from individual medical devices, reliance on physical spot checks of patients, and the lack of rules-based advanced analytics to assess a patient\u2019s current condition in real-time or identify signs of deterioration puts that achievement out of reach for many hospitals and health systems.<\/p>\n

There are a number of hospital-acquired conditions (HACs) that could be prevented by continuous clinical surveillance. Sepsis and respiratory compromise are among the most costly in terms of resources , morbidity and mortality.<\/p>\n

For example, respiratory failure that requires emergency mechanical ventilation occurs in 44,000 patients per year in the United States.1<\/sup> The cost to U.S. hospitals for opioid-induced respiratory depression (OIRD) interventions are estimated at nearly $2 billion per year.2<\/sup> In addition, ventilator-associated complications (VAC) can lead to longer stays in the ICU and greater rates of readmission. VAC adds approximately $40,000 in costs to each case\u2014or $1.2 billion in total costs annually.3<\/sup><\/p>\n

Complications due to OIRD are not confined to the ICU, either. In a 2014 study, Slight et al., found that between 2008 and 2012, more than 10,000 patients who suffered an in-hospital cardiopulmonary arrest (IHCA) did so on the general care floor, which is where patients with relatively stable conditions are placed.4<\/sup><\/p>\n

Trending Toward Best Practices<\/strong><\/p>\n

Continuous surveillance is the obvious best practice\u2014and one recommended by the Centers for Medicare and Medicaid Services5<\/sup>, the Joint Commission6<\/sup>, the Anesthesia Patient Safety Foundation7<\/sup> and other healthcare advocates and agencies. Though it is more often utilized in high-acuity settings, such as intensive care units, ever-increasing emphasis on detecting potential adverse clinical events makes enterprise-wide continuous surveillance an enticingsolution.<\/p>\n

The emerging utilization of real-time data to drive continuous surveillance offers health systems a quantitative estimate of whether a patient\u2019s condition is going to get worse over time. In contrast to traditional patient monitoring, continuous surveillance is a systematic, goal-directed process that detects physiological changes in patients early, interprets the clinical implications of those changes and alerts clinicians so they can intervene rapidly.8<\/sup><\/p>\n

Data collection and analysis are further enhanced when including methods for disseminating, analyzing, and distributing these data. These features facilitate better patient care management and clinical workflow by allowing patients to be monitored remotely.<\/p>\n

Return on Investment<\/strong><\/p>\n

More than a patient safety measure, continuous surveillance is a viable and sustainable solution to the negative costs associated with patient deterioration9<\/sup>, including resource utilization, emergency transfers to ICUs, length of stay and hospital readmissions.<\/p>\n

Early Intervention<\/em>. <\/strong>In a 21-month study on the impact of pulse oximetry surveillance on rescue events and ICU transfers, Taenzer et al., observed that continuous surveillance techniques decreased rescue events from 3.4 to 1.2 per 1,000 patient discharges and ICU transfers from 5.6 to 2.9 per 1,000 patient days.10<\/sup><\/p>\n

In a study to determine the efficacy of continuous capnography monitoring on emergency rescues, Stites et al., observed that \u201cthe pre-intervention incidence of OIRD in the setting of rapid response was 0.04% of patients receiving opioids. After the implementation of capnography, the incidence of OIRD in the setting of rapid response was reduced to 0.02%, which was statistically significant.\u201d11 <\/sup>In addition, the authors found that continuous surveillance also reduced transfers to higher levels of care was reduced by 79% (baseline, 7.6 transfers\/month; post-intervention, 1.6 transfers\/month).<\/p>\n

Length of Stay<\/em>. <\/strong>There are a number of studies that point to continuous clinical surveillance resulting in a statistically significant impact on a patient\u2019s length of stay (LOS) in a hospital.<\/p>\n

During an 18-month clinical trial in a 33-bed inpatient MED-SURG unit, Brown et al., observed that \u201ccontinuous monitoring on a [MED-SURG] unit was associated with a significant decrease in total length of stay in the hospital and in intensive care unit days for transferred patients, as well as lower code blue rates.\u201d12<\/sup><\/p>\n

Technology<\/strong>. Continuous surveillance can be accomplished largely with modest net-new technology investments; hospitals with critical care units or ICUs already have continuous surveillance infrastructure in place.13<\/sup> Optimizing that infrastructure\u2019s capabilities and incorporating it into existing clinical workflows is the real heavy lift.<\/p>\n

Continuous monitoring from multiple data sources\u2014EKGs, vital signs, laboratory tests\u2014will yield better predictive models than data from a single source. One of the goals of the advanced analytics that come with continuous clinical surveillance is to connect the dots from among seemingly unrelated, individual data sources. This ability enables clinicians to observe and predict a potentially adverse course in the patient\u2019s condition over time, prior to the violation of the limit threshold of any individual parameter, and respond before alarm floods and costly interventions are required.<\/p>\n

Starting Point<\/strong><\/p>\n

EHRs form the foundation of how most hospitals are approaching surveillance\u2014and make for a natural starting point. For example, EHRs store retrospective data, but there is value to augmenting surveillance strategies by adding real-time data captured from patient-connected devices. For example, real-time clinical surveillance and analytics solutions can collect and aggregate clinician-validated retrospective data from the EHR including patient demographics and lab values, and correlate it with real-time streaming data including temperature, heart rate, oxygenation levels and blood pressure.<\/p>\n

Analytics based on multiple sources of data also can help offset the problem of alarm fatigue by filtering out false or artifact signals that typically invade the high-fidelity data at the core of continuous surveillance.<\/p>\n

Beyond high-acuity areas, healthcare systems are creating a foundation for other real-time healthcare innovations, including clinical surveillance modules, medical device integration in an EHR and virtual ICUs.<\/p>\n

Combining analysis with real-time data at the point of collection creates a powerful tool for prediction and clinical decision support. The ability to track patients throughout the hospital, continuously add new devices, and distribute real-time patient monitoring to centralized dashboards and mobile devices should be a major consideration for CIOs tasked with achieving real-time healthcare capabilities.<\/p>\n

<\/h1>\n<\/div><\/section><\/div>

References<\/strong><\/h3>\n
    \n
  1. Respiratory Compromise as a New Paradigm for the Care of Vulnerable Hospitalized Patients Timothy A Morris MD, Peter C Gay MD, Neil R MacIntyre MD FAARC, Dean R Hess PhD RRT FAARC, Sandra K Hanneman PhD RN, James P Lamberti MD, Dennis E Doherty MD, Lydia Chang MD, and Maureen A Seckel APRN RESPIRATORY CARE \u2022 APRIL 2017 VOL 62 NO 4<\/li>\n
  2. AAMI Foundation. Opioid Safety & Patient Monitoring Conference Compendium. November 14, 2014. Available at: http:\/\/s3.amazonaws.com\/rdcms-aami\/files\/production\/public\/Libraries\/Opioids\/Opioid_Compendium_2015.pdf<\/a>.<\/li>\n
  3. https:\/\/acphospitalist.org\/archives\/2013\/11\/coding.htm<\/a><\/li>\n
  4. Slight SP, Franz C, Olugbile M, Brown HV, Bates DW, Zimlichman E. Crit Care Med. 2014 ;42.<\/li>\n
  5. U.S. Department of Health & Human Services. Requirements for hospital medication administration, particularly intravenous (IV) medications and post-operative care of patients receiving IV opioids. Centers for Medicare and Medicaid Services. March 14, 2014. Available at: www.cms.gov\/Medicare\/Provider-Enrollment-and-Certification\/SurveyCertificationGenInfo\/Downloads\/Survey-and-Cert-Letter-14-15.pdf<\/a>.<\/li>\n
  6. The Joint Commission. Joint Commission enhances pain assessment and management requirements for accredited hospitals. Perspectives. 37(7); July 2017. Available at: jointcommission.org\/assets\/1\/18\/Joint_Commission_<\/a>Enhances_Pain_Assessment_and_Management_
    \nRequirements_for_Accredited_Hospitals1.PDF.<\/a><\/li>\n
  7. Becker\u2019s Hospital Review. New CMS guidance recommends monitoring of all patients receiving opioids: what it means for healthcare providers. June 2, 2014. Available at: www.beckershospitalreview.com\/quality\/new-cms-guidance-recommends-monitoring-of-all-patients-receiving-opioids-what-it-means-for-healthcare-providers.html<\/a>.<\/li>\n
  8. Giuliano, Karen K. \u201cImproving Patient Safety through the Use of Nursing Surveillance.\u201d AAMI Horizons. Spring 2017, pp 34-43.<\/li>\n
  9. Calzavacca P, Licari E, Tee A, Egi M, Haase M, Haase-Fielitz A, Bellomo R. A prospective study of factors influencing the outcome of patients after a Medical Emergency Team review. Intensive Care Med<\/em>. November 2008;34(11):2112-6. Available at: www.ncbi.nlm.nih.gov\/pubmed\/18651130<\/a>.<\/li>\n
  10. Taenzer AH,\u00a0Pyke JB,\u00a0McGrath SP,\u00a0Blike GT. Impact of pulse oximetry surveillance on rescue events and intensive care unit transfers: a before-and-after concurrence study. Anesthesiology<\/em>.\u00a02010 Feb;112(2):282-7.<\/li>\n
  11. Stites M, Surprise J, McNiel J, Northrop D, De Ruyter M. Continuous capnography reduces the incidence of opioid-induced respiratory rescue by hospital rapid resuscitation team. J Pat Saf. July 2017.<\/li>\n
  12. Brown H, Terrence J, Vasquez P, Bates DW, Zimlichman E. Continuous monitoring in an inpatient medical-surgical unit: a controlled clinical trial. Am J Med<\/em>. 2014 Mar;127(3):226-32.<\/li>\n
  13. Zaleski J. Putting data to work: hospitals are awash in patient data\u2014why is so much of it never put to use? Inside Big Data<\/em>. February 1, 2018. Available at: https:\/\/insidebigdata.com\/2018\/02\/01\/putting-data-work-hospitals-awash-patient-data-much-never-put-use<\/a>.<\/li>\n<\/ol>\n<\/div><\/section><\/div><\/p>\n
    <\/span><\/span><\/div>\n

    About the Author:<\/strong>
    \n\"\"<\/p>\n

    Brian McAlpine,
    \nVice President Strategy and Business Development<\/p>\n

    Brian has over 20 years of experience in healthcare IT solutions with a focus on medical device integration, alarm safety, clinical mobility and workflow solutions. Prior to Bernoulli, Brian served in executive leadership and strategic roles at Extension Healthcare (now Vocera), Capsule Tech, Philips-Emergin, Siemens Medical, Draeger Medical, and HP Medical (now Philips Healthcare). Brian holds a BS degree in Electrical Engineering from the University of Massachusetts Lowell. View Brian\u2019s profile on Linkedin<\/a>.<\/p>\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"

    \t\t\t\tThe successful implementation of real-time patient safety initiatives has long been a goal of health system leaders. Unfortunately, parsing alarms from individual medical devices, reliance on physical spot checks of patients, and the lack of rules-based advanced analytics to assess a patient\u2019s current condition in real-time or identify signs of deterioration puts that achievement out of reach for many hospitals and health systems.\t\t<\/p>\n","protected":false},"author":5,"featured_media":8499,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[322],"tags":[376,394],"_links":{"self":[{"href":"https:\/\/www.light4soul.com\/testingsite\/cardiop\/wp-json\/wp\/v2\/posts\/8498"}],"collection":[{"href":"https:\/\/www.light4soul.com\/testingsite\/cardiop\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.light4soul.com\/testingsite\/cardiop\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.light4soul.com\/testingsite\/cardiop\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/www.light4soul.com\/testingsite\/cardiop\/wp-json\/wp\/v2\/comments?post=8498"}],"version-history":[{"count":0,"href":"https:\/\/www.light4soul.com\/testingsite\/cardiop\/wp-json\/wp\/v2\/posts\/8498\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.light4soul.com\/testingsite\/cardiop\/wp-json\/wp\/v2\/media\/8499"}],"wp:attachment":[{"href":"https:\/\/www.light4soul.com\/testingsite\/cardiop\/wp-json\/wp\/v2\/media?parent=8498"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.light4soul.com\/testingsite\/cardiop\/wp-json\/wp\/v2\/categories?post=8498"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.light4soul.com\/testingsite\/cardiop\/wp-json\/wp\/v2\/tags?post=8498"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}