Early deterioration in adult medical-surgical patients is associated with increased intensive care unit and hospital mortality (Goldhill, 2001). Failure to recognize deterioration is a preventable patient safety and quality issue. To address this problem, since 2013, Kaiser Permanente Northern California (KP NCAL) has piloted Advance Alert Monitor (AAM) at two hospitals. This early warning system employs a set of predictive models developed by the KP NCAL Division of Research, which automatically predicts patient deterioration within the next 12 hours based on a complex algorithm of laboratory and clinical data points. Improvements in mortality and length of stay have been realized at the two pilot hospitals. In anticipation of expansion to additional NCAL facilities, major changes to the AAM workflows and processes were developed that increased the sensitivity of the patients identified at risk for clinical deterioration, as well as the timeliness and clarity of clinical response. Expansion to two additional pilot hospitals using these revised processes rely on the evidence-based implementation strategies found in this Doctor of Nursing Practice project. This paper examines the planning, assessment, and implementation of early warning systems at two NCAL facilities using Rogers’ diffusion of innovation theory and Greenhalgh’s extension of Rogers’ theory.
Key attributes need to be considered from a cultural and organizational perspective to both start and sustain an implementation. The success of AAM implementation is validated using specific outcome and process measures, including compliance with documentation and timeliness of workflows.
","abstract_html":"<p>Early deterioration in adult medical-surgical patients is associated with increased intensive care unit and hospital mortality (Goldhill, 2001). Failure to recognize deterioration is a preventable patient safety and quality issue. To address this problem, since 2013, Kaiser Permanente Northern California (KP NCAL) has piloted Advance Alert Monitor (AAM) at two hospitals. This early warning system employs a set of predictive models developed by the KP NCAL Division of Research, which automatically predicts patient deterioration within the next 12 hours based on a complex algorithm of laboratory and clinical data points. Improvements in mortality and length of stay have been realized at the two pilot hospitals. In anticipation of expansion to additional NCAL facilities, major changes to the AAM workflows and processes were developed that increased the sensitivity of the patients identified at risk for clinical deterioration, as well as the timeliness and clarity of clinical response. Expansion to two additional pilot hospitals using these revised processes rely on the evidence-based implementation strategies found in this Doctor of Nursing Practice project. This paper examines the planning, assessment, and implementation of early warning systems at two NCAL facilities using Rogers’ diffusion of innovation theory and Greenhalgh’s extension of Rogers’ theory. </p> <p>Key attributes need to be considered from a cultural and organizational perspective to both start and sustain an implementation. The success of AAM implementation is validated using specific outcome and process measures, including compliance with documentation and timeliness of workflows. </p>","abstract_has_math":false,"creators":["Paulson, Shirley S"],"institution":null,"degree_name":"Doctor of Nursing Practice (DNP)","degree_level":"Project","degree_discipline":"Nursing","degree_department":null,"school":null,"contributors":["Keith Dawson, DNP, MS, RN, NEA-BC","Mary Lynne Knighten, DNP, RN, NEA-BC"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-03T07:00:00Z","date_published":"2017-08-03T07:00:00Z","updated_at":"2026-07-24T05:44:13Z","subjects":["Predictive Analytics","rapid response","early warning system","diffusion of innovation","Analytical, Diagnostic and Therapeutic Techniques and Equipment","Critical Care","Critical Care Nursing","Telemedicine"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repository.usfca.edu/dnp/105","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Keith Dawson, DNP, MS, RN, NEA-BC","Mary Lynne Knighten, DNP, RN, NEA-BC"]},{"key":"dc:creator","label":"Author","values":["Paulson, Shirley S"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2017-11-29T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Nursing"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Project"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Nursing Practice (DNP)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Predictive Analytics","rapid response","early warning system","diffusion of innovation","Analytical, Diagnostic and Therapeutic Techniques and Equipment","Critical Care","Critical Care Nursing","Telemedicine"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://repository.usfca.edu/dnp/105"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Early deterioration in adult medical-surgical patients is associated with increased intensive care unit and hospital mortality (Goldhill, 2001). Failure to recognize deterioration is a preventable patient safety and quality issue. To address this problem, since 2013, Kaiser Permanente Northern California (KP NCAL) has piloted Advance Alert Monitor (AAM) at two hospitals. This early warning system employs a set of predictive models developed by the KP NCAL Division of Research, which automatically predicts patient deterioration within the next 12 hours based on a complex algorithm of laboratory and clinical data points. Improvements in mortality and length of stay have been realized at the two pilot hospitals. In anticipation of expansion to additional NCAL facilities, major changes to the AAM workflows and processes were developed that increased the sensitivity of the patients identified at risk for clinical deterioration, as well as the timeliness and clarity of clinical response. Expansion to two additional pilot hospitals using these revised processes rely on the evidence-based implementation strategies found in this Doctor of Nursing Practice project. This paper examines the planning, assessment, and implementation of early warning systems at two NCAL facilities using Rogers’ diffusion of innovation theory and Greenhalgh’s extension of Rogers’ theory.
Key attributes need to be considered from a cultural and organizational perspective to both start and sustain an implementation. The success of AAM implementation is validated using specific outcome and process measures, including compliance with documentation and timeliness of workflows.
"]},{"key":"dc:title","label":"Title","values":["Implementation of an Innovative Early Warning System: Evidenced-based Strategies for Ensuring System-wide Nursing Adoption"]}]}],"canonical_facts":{"dc:contributor":["Keith Dawson, DNP, MS, RN, NEA-BC","Mary Lynne Knighten, DNP, RN, NEA-BC"],"dc:creator":["Paulson, Shirley S"],"dc:date.available":["2017-11-29T08:00:00Z"],"dc:description.abstract":["Early deterioration in adult medical-surgical patients is associated with increased intensive care unit and hospital mortality (Goldhill, 2001). Failure to recognize deterioration is a preventable patient safety and quality issue. To address this problem, since 2013, Kaiser Permanente Northern California (KP NCAL) has piloted Advance Alert Monitor (AAM) at two hospitals. This early warning system employs a set of predictive models developed by the KP NCAL Division of Research, which automatically predicts patient deterioration within the next 12 hours based on a complex algorithm of laboratory and clinical data points. Improvements in mortality and length of stay have been realized at the two pilot hospitals. In anticipation of expansion to additional NCAL facilities, major changes to the AAM workflows and processes were developed that increased the sensitivity of the patients identified at risk for clinical deterioration, as well as the timeliness and clarity of clinical response. Expansion to two additional pilot hospitals using these revised processes rely on the evidence-based implementation strategies found in this Doctor of Nursing Practice project. This paper examines the planning, assessment, and implementation of early warning systems at two NCAL facilities using Rogers’ diffusion of innovation theory and Greenhalgh’s extension of Rogers’ theory.
Key attributes need to be considered from a cultural and organizational perspective to both start and sustain an implementation. The success of AAM implementation is validated using specific outcome and process measures, including compliance with documentation and timeliness of workflows.
"],"dc:identifier":["https://repository.usfca.edu/dnp/105"],"dc:subject":["Predictive Analytics","rapid response","early warning system","diffusion of innovation","Analytical, Diagnostic and Therapeutic Techniques and Equipment","Critical Care","Critical Care Nursing","Telemedicine"],"dc:title":["Implementation of an Innovative Early Warning System: Evidenced-based Strategies for Ensuring System-wide Nursing Adoption"],"thesis:degree_discipline":["Nursing"],"thesis:degree_level":["Project"],"thesis:degree_name":["Doctor of Nursing Practice (DNP)"]},"updated_at":"2026-07-24T05:44:13Z"}