Identifying-the-continuous-vital-sign-alarm-patterns

1 “Identifying the continuous vital sign alarm patterns for three potentially lethal complications in general ward patients.” (JHM IRB00188931 full study submitted) Principal Investigator Co-Investigator Sue Carol Verrillo, DNP, RN, CRRN Assistant Professor Johns Hopkins School of Nursing 525 North Wolfe Street, Rm 453 Baltimore, MD 21205 O.(410)614-2418|E sverril1@jhmi.edu Bradford D Winters, Ph.D., M.D., FCCM Associate Professor Anesthesiology and Critical Care Medicine/Surgery Division Director, Critical Care Medicine Co-Director, Surgical Intensive Care Unit Core Faculty Armstrong Institute for Patient Safety/Quality Johns Hopkins Hospital 1800 Orleans Street Baltimore, MD 21287 O.(410)955-9080|E bwinters@jhmi.edu Abstract Purpose and Aims: The purpose and main aim of this research study is to use artificial intelligence to analyze the patient’s de-identified vital signs to identify the vital sign alarm pattern for a pulmonary embolism (PE), an occult gastrointestinal bleed (GIB) and early systemic inflammatory response syndrome (SIRS) to prevent failure to rescue deaths The second aim is collect outcome data for the incidence of complications, based on the top ten Maryland Hospital Acquired Conditions (MHACs), number of RRT calls, number of transfers to an ICU and number of failure to rescue deaths Rationale/Significance: It has been reported in HealthGrades that one out of every ten patients currently dies from either a PE/DVT, pneumonia/sepsis, shock/cardiac arrest or a GIB (Reed, May, Nicholas, 2013) Payouts and settlements from these complications can cost healthcare institutions millions of dollars Vital sign alarm patterns could be used in a decision support tool to prevent costly complications, such as a PE Theoretical Framework: The Knowledge to Action Framework (Tetroe, Graham, 2009) The cyclic process provides nursing with the tools to identify the problem, assesss the barriers, implement an intervention and monitor its use for sustainability Study variables will include (1) identifying the consistently true vital sign alarm pattern for a PE, an occult GIB and SIRS and (2) collecting the incidence of unit-based complication rate (per 1000 patient days), number of Rapid Response Team calls, number of transfers to an ICU and number of failure to rescue deaths Patient and staff satisfaction surveys will be an additional variable Design/Setting/Sample/Methods and Implications for Practice: This research study will be a prospective, pretest/post-test design of two independent samples at the Johns Hopkins Hospital, Zayed 11 East, a 32-bed Ortho/Ortho-Spine/Trauma Unit Since cVSM is the standard of care, all patients admitted to the general ward will be connected to the continuous vital sign monitor, as indicated in the admission orders Permission to treat forms signed during the admission process will be the patient consenting process to obtain standard of care vital signs through the Sotera ViSi continuous vital sign monitor De-identified vital signs will be collected daily and sent for high level real-time analysis to identify any alarm patterns for the identified complications, and be clinically correlated with lab values, radiographic results and physiologic assessments to see if the alarm pattern is congruent with the patient’s physiologic presentation Using high fidelity vital signs alarm pattern recognition will empower staff and advanced practice trauma nurses to save lives through moving the paradigm from retrospectively treating life threatening complications to predicting the imminent occurrence of these complications thus saving lives, resources and millions of dollars Purpose and Specific Aims Purpose: According to Bansal et al (2009), patients deaths from trauma, in the first six hours are mainly due to chest and abdominal trauma with hemorrhage and after 12 hours are due to brain injury, sepsis and multi-organ system failure The in-patient Ortho/Ortho-Spine/Trauma Unit at the Johns Hopkins Hospital is a Level Trauma Unit, and in FY18, of the 879 inpatients, 61% were gunshot wounds, 4% were stabbings and 17% were personal injury due to falls The healthcare team has one to twelve hours to treat and stabilize the patient to prevent sudden death Vital sign data on an ortho/ortho-spine/trauma general ward is what the healthcare team depends on to know what the patient’s hemodynamic status is in real-time Having the highest fidelity data with the ability to trend the data and identify the alarm pattern for a PE, GIB and SIRS would help the team detect early deterioration and use the vital sign alarm patterning to predict that a complication is beginning to develop The money from this grant would be used toward funding the Consultants who have the logarithms and relational database to analyze, on a very high level, the raw, de-identified vital sign data to identify the vital sign alarm patterns These identified patterns would be correlated in real time with the patient’s physiologic assessments, including lab values and radiographic results The money would also go toward funding biostatistical support for data analysis Time is of the essence when treating trauma patients, therefore identifying occult hemorrhage, PE and SIRS/Sepsis could save many lives and resources Aim Compare the retrospective alarm data from the continuous vital sign monitoring (cVSM) pilot, conducted from December 11,2015 – March 8, 2016, to determine if the anecdotal alarm patterns for PE, occult GIB and SIRS are consistent with what the prospective data is presenting Strategy 1.1: Trend the alarm patterns over the current study time period of March 11, 2019 – March 11, 2020 to see if the pattern consistently holds true for each of the three potentially lethal complications Strategy 1.2: Vital signs will be uploaded from EPIC into Sotera ViSi Mobile® AWS® Cloud Sotera will send the de-identified vital signs to the Independent Contractor, who will download the deidentified vital signs and analyze them through the proprietary logarithms and relational database in order to see the changes in momentum, velocity and rate of change to identify the true and consistent alarm pattern for a PE, occult GIB and SIRS Aim Strategy 1.3: Collect patient outcome measures of overall complication rate (Based on MHACs), number of RRT calls, number of transfers to an ICU and the failure to rescue death rate Distribute the Sotera ViSi Mobile® patient satisfaction survey to each patient on the day of discharge Strategy 2.1: Retrospectively use the Sotera patient satisfaction scores from the pilot study done from December 11, 2015 – March 8, 2016 as baseline data (Verrillo, Cvach, Hudson, Winters, 2018) We will validate the survey tool in this prospective population Strategy 2.2: The patient’s nurse will include Sotera’s paper survey as part of the discharge planning process and ask the patient if they would complete the survey, which will be Likert scale questions and will have a final comment /free text box Since discharges can happen 24/7, each completed survey will be placed into a manila envelope, sealed and addressed to the PI A file holder will be placed in the back of the Central Nurses’ Station, next to the RVD to collect completed patient satisfaction surveys Strategy 2.3: Use biostatistics support services to analyze the patient satisfaction data Aim 3: Distribute the Sotera ViSi Mobile® staff satisfaction survey to each staff member, at the week mark after initiation, then at the month, month, month and 12 month marks Strategy 3.1: Retrospectively use the Sotera staff satisfaction survey scores from the pilot study done from December 11, 2015 – March 8, 2016 as baseline data We will validate the survey in this prospective population Strategy 3.2: Surveys will be sent to each staff member electronically through the Qualtrics® System Demographics, and separate topics such as the functionality, effectiveness, ease of use and safety will be asked through a Likert scale Employees will be given week to complete the survey Strategy 3.3: Use biostatistical support services to analyze the staff satisfaction data Significance to Trauma Nursing/ Framework/ Review of Literature Significance During the cVSM pilot, trauma patients accounted for 36% of the patient population, (Orthopaedics – 43%, Ortho-Spine 8%, Other Services – 13%, internal data), these numbers were consistent throughout the fiscal year, with seasonal adjustments Continuous vital sign monitoring has the potential to transform trauma nursing practice/decision-making by replacing static, episodic manual data with high fidelity continuous data that is dynamic and available through the electronic medical record Vital sign alarms can be trended through a Remote Viewing Device (RVD) for up to 72 continuous hours According to the results of the Casey-Fink survey (given to all new graduates who are hired at Johns Hopkins Hospital throughout their first year Nurse Residency Program), the new graduate’s top three concerns are (1)not having any confidence in their ability, (2) fear of harming their patient and (3) not being able to handle the workload (See Appendix H, Figure 1) With cVSM, new graduate nurses will have streaming data with smartphone alerts to determine their patient’s status so they can prioritize their nursing care according to which patients are completely stable, mostly stable versus not being stable This knowledge alone will also prevent the new graduate from not being aware of early patient deterioration increase their confidence, thus they will be less likely to harm their patient and they will handle their workload more effectively This knowledge could make a life or death difference in the trauma patient’s status Framework As described in the Abstract, the Knowledge to Action Framework (Tetroe, Graham, 2009) will be used for critically thinking through what the knowledge acquisition of continuous vital sign data means to individual patient care Using cVSM will be a practice change for the nurses, who have been hired since the original pilot They will be given training, mentoring and real-time feedback from the Unit Based Educator and the Senior Nurses who were present during the pilot and can act as “Unit Super Users” This will assist the staff to move from the novice level of going through the motions of using the cVSM to competent, then expert level by incorporating the depth of knowledge the cVSM is giving into their critical decision making, especially regarding trauma patients Then perceived barriers and benefits to using of the monitor will be able to be addressed in the moment The study team will perform cQI and QA to make sure that all patients and staff have an optimized experience of cVSM Review of Literature Taenzer, Pyke and McGrath (2011) stated that no single vital sign parameter can predict all types of deteriorationall five vital sign parameters are needed to detect all possible types of deterioration Lynn and Curry (2011) stated that subtle changes in cardiopulmonary status are not being picked up soon enough by clinicians to prevent patient deterioration Liu, Kipnis, Rizk and Escobar (2012) echoed this work by documenting that the majority of unplanned transfers to an ICU happen within the first 48 hours of ward admission, with the highest risk for postoperative complications in the first 24 hours This study will bridge the knowledge gap by replacing manual episodic vital sign collection (eVSC) with cVSM Higher fidelity data with the ability to trend the data will give nursing richer and fuller data, that coupled with physical assessments and lab/radiographic data will enable the bedside clinicians to make better informed decisions about patient care and anticipate more accurately the status of the patient However, the biggest knowledge gap clearly is the lack of the identity of the distinct and consistently true vital sign alarm pattern for three distinct potentially lethal complications for postoperative trauma patients: pulmonary embolism, occult gastrointestinal bleed (i.e a retroperitoneal bleed) and early systemic inflammatory response syndrome (SIRS) that could move vital sign collection and trending from early detection to predicting deterioration Treatment could then stop any further development of the complication before it even has a chance to gain a viable presence in the patient’s physiological status Preliminary Work Johns Hopkins Zayed 11 East Unit specific baseline data was analyzed for a 12 week period prior to the pilot study This convenience sample revealed a 22% complication rate and there were recorded failure to rescue deaths in the Johns Hopkins Hospital CPR Committee database During this time period routine manually collected, episodic, vital signs were the standard of care These vital signs were single data points without any trending capabilities When the 12 week intervention of cVSM was instituted during the pilot project, the data showed a decrease in the complication rate from 22% to 5.9% - a 73% decrease Further analysis showed that the risk of developing a complication also dropped 27% The failure to rescue rate decreased to zero Patient satisfaction showed a 74% “strongly agree” rating that cVSM would alert the nurses if there was a change in their health status Staff satisfaction improved from an initial 82% to a final 93% Methods and Design Design This full research study will be a prospective, pre-test/post-test design of two independent samples Sample and Setting The Johns Hopkins Hospital, Zayed 11 East, which is a 32-bed Ortho/Ortho-Spine/Trauma Unit Since cVSM will be the standard of care, all patients admitted to the general ward will be connected to the continuous vital sign monitor, as indicated in the admission orders Since using the Sotera ViSi monitor to obtain vital signs was approved as the standard of care on Zayed 11 East by the hospital leadership and administration, consent to treat forms done during the admission process, when permission to treat consents are signed, will cover consenting the patient to obtain standard of care routine vital signs through the Sotera ViSi continuous vital sign monitor The initial power analysis required a minimum of 306 patients during the three month pilot, but since this in now the standard of care all patients admitted to the unit will be monitored for the entire year The unit typically admits approximately 1200 patients per year Algorithms to respond to vital sign alarms have been established with the vendor and the nursing staff have been educated regarding their responsibilities for responding to vital sign alarms Alarm limits and annunciation delays are set per vendor and institution standards A continuous vital sign policy has been created, reviewed by the Johns Hopkins Hospital Alarm Management Committee and published on Hopkins Policies On-Line (HPO) Intervention/ Independent Variables The PI will collect pre-intervention and post-intervention demographic data: Mean Age (SD;95% Confidence Level); Gender; Service; and % of Patients using Patient Controlled Analgesia and outcome measures: prevalence and incidence of complications ; RRT calls; transfer to an ICU and failure to rescue deaths All patients will be put on the monitor, as part of their consent to treat The exclusion criteria will include that the patient’s upper extremities are immobilized or injured and have splints/ casts or the upper extremities are amputates and therefore the patient is not able to have the continuous vital sign monitoring leads applied If patients are admitted to hospital they typically cannot refuse to have vital signs taken as it is part of the standard of care If the patient does refuse, we will report this to the attending of record within one hour of admission Instruments Sotera ViSi Mobile® provided a validated, reliable Staff Satisfaction Survey that we could personalize for distribution to our staff We will use the same survey for this study For data collection, the pilot PI and co-Investigator did all of the data collection, the same data collection tool was used by both data collectors and they compared notes daily to make sure that they had interrater agreement on collecting the same data points and could correct any unintentional human recording errors The same tool will be used for this study and they will collect the same data points for the outcome measures However, the new data, will be sending the de-identified patient’s vital signs from Sotera’s AWS® Cloud storage to the Independent Contractor who will be analyzing the vital signs and sending the analysis back for cVSM alarm patterns (See Data Collection Schedule and Procedures section) Data Collection Schedule and Procedures For pre-intervention data collection, the Johns Hopkins Hospital CPR Committee will provide the Zayed 11 East data from March 10, 2018 – March 10, 2019 for the number of RRT calls, the number of patients transferred to an ICU and the number of failure to rescue deaths A request to the Department of Surgery Administrator for the number of Patient Days and to the hospital coders for the number of complications (based on the top ten MHACs) Nursing staff will be receiving training from the vendor the week before the go-live in how to correctly apply the monitor, and the expectations for checking the patient when an alarm notification comes to their smartphone They are to check the patient immediately to determine if the alarm is true or false and make a notation in the patient’s electronic record For the intervention group, the PI or the co-Investigator will be collecting the data everyday between 0800 – 1100 for outcome measures from the Remote Viewing Device (RVD) and capturing any further data from the patient’s electronic record for interventions via the electronic memos in EPIC The following data points will be collected: Date (mm/dd/yyy), time of day (Military format), number of patients being actively monitored (numerator)/ unit census as of midnight(denominator), patient rooms will be listed in numeric order starting with 11040 through 11073 If any rooms are closed it will be noted on the form Then starting with Room 11040, the PI or co-Investigator will systematically scroll down room by room and note the patient’s name, Medical Record Number, Unique Device Number, Admission Date/Time, age, service, and individually record the time and type of each alarm The PI or co-investigator will then go into the electronic medical record to perform a hand chart review to see if the nursing staff wrote any electronic memos in the vital sign section or noted any interventions that were done (i.e., team notified, orders received) to ascertain if the patient was stabilized and kept on the unit, was an RRT called, was the patient transferred to an ICU, or did the patient die The top ten MHACs will be used to categorize the annotated complications, the PI or co-investigator will a hand chart review for evidence of documented complications The request from the Department of Surgery Administrator for the monthly Patient Days data and official census will be submitted We will repeat daily data collection every day, in the same format The data will be entered into the Safebox database for security and protection of data The data collection tools will be kept in a locked drawer in the PI’s locked private office as source documents The PI will train Zayed 11 East Staff RNs to be back-up data collectors for weekends, holidays and in case either the PI or co-Investigator are sick or out of town at conferences or meetings The PI or co-Investigator will monthly Quality Control checks by reviewing the substitute data collector’s data collection methods for accuracy Any re-training will be done and documented if needed For vital sign analysis for alarm patterns: Sotera will download the de-identified vital signs into the Independent Contractor’s secure database for logarithmic analysis in a relational database The results will be reported back daily to the PI Any clinically urgent data from alarm analysis will be reported back within hours of discovery Data Analysis and Interpretation Data will be entered into SPSS®, using systematic variable coding as was done during the pilot Demographics will be analyzed using the measures of central tendency for age, gender and service The pre-intervention demographic data will be compared to the intervention demographic data for their degree of homogeneity Pre-intervention and intervention data will be compared through Chi-square statistical analysis for significance Vital sign alarm pattern analysis will be done through a proprietary logarithms and run through a relational database for momentum, velocity, speed of change, direction of change Preliminary alarm patterns will be documented in a secure database in Safebox After receiving the vital sign alarm pattern, it will be correlated with the patient’s labs, radiographic results, and physical assessment If the alarm pattern for a pulmonary embolism, occult gastrointestinal bleed or early systemic inflammatory response syndrome is confirmed by the other aforementioned findings, the PI and co-Investigator will trend these findings during the study to determine the prevalence and incidence of reoccurring vital sign alarm patterns This will be new knowledge, as no one has done this level of vital sign analysis on general ward patients before Facilities and Environment Johns Hopkins Hospital East Baltimore: An urban academic 1,000 bed medical center that encompasses a block radius in all directions There are institutions that comprise Johns Hopkins Health System- Sibley Memorial Hospital, Suburban Hospital, Howard County General Hospital, Johns Hopkins Bayview Medical Center, Johns Hopkins Hospital East Baltimore, and All Children’s Hospital in FL Johns Hopkins University School of Nursing Biostatistics Consulting Services: Assistance included with statistical aspects of grant proposal preparation, research study design, power analysis and determination of sample size, randomization, data management, statistical modeling, data analysis and interpretation, and statistical software assistance Short-term assistance is provided as a free service to School of Nursing researchers This may include a one-time consultation or a series of meetings Assistance with statistical aspects of grant proposal writing is available Implications for Practice and Research Future research will most certainly drill down even further into prediction of patient deterioration by identifying alarm patterns for other types of complications Just as the first project laid the groundwork for this phase by consistently detecting early signs of deterioration in adult postoperative trauma patients Now we are moving from early detection of deterioration to predicting deterioration through identifying the vital sign alarm patterns of three potentially lethal complications This work certainly will need to be deepened through further data collection and expanding the number of complications that have true, reliably identified vital sign alarm patterns These study findings will be disseminated through publications in peer reviewed journals, and presentations at various conferences and meetings, including the Society of Trauma Nurses 1    Appendix A References Bansal, V., Fortlage, D., Lee, J.G, Costantini, T., Potenza, B., Coimbra, R (2009) Hemorrhage is More Prevalent than Brain Injury in Early Trauma Deaths: The Golden Six Hours European Journal of Trauma and Emergency Surgery, 35(1):26-30 Doi:10.1007/s00068-008-8080-2 Centers for Medicare and Medicaid Services, editor (2013) Medicare program: hospital inpatient prospective payment systems for acute care hospitals and the long term care hospital prospective payment system and Fiscal Year 2013 rates; quality reporting policies related to patient status Final Rules Federal Registry, 78(160),50495-51040 Association between obstructive sleep apnoea and postoperative outcome British Journal of Anaesthesia, 109(6):897-906 doi: 10.1093/bja/aes/308 Liu, V., Kipnis, P., Rizk, N.W., Escobar, G.J (2012) Adverse Outcomes Associated With Delayed Intensive Care Unit Transfers in an Integrated Healthcare System Journal of Hospital Medicine, 7(3):224-230 Lynn, L.A., Curry, J.P (2011) Patterns of unexpected in-hospital deaths: a root cause analysis Patient Safety In Surgery, 5(3):1-24 Reed, K., May, R., Nicholas, C (Eds) (2013) Health Grades: Patient Safety in American Hospitals Study Denver: Health Grades, Inc Maddox, R.R., Oglesby, H., Williams, C.K., Fields, M., D’Anello, S (2008) Continuous Respiratory Monitoring and a “Smart” Infusion System Improve Safety of Patient-Controlled Analgesia in the Postoperative Period Technology and Medication Safety, 4:1-13 Straus, S.E., Tetroe, J., Graham, I (2009) Defining knowledge translation Canadian Medical Association Journal, 181(3-4):165-168 Taenzer,A.H., Pyke, J.B., McGrath, S.P (2011) A Review of Current and Emerging Approaches to Address Failure-to-Rescue Anesthesiology, 115(2): 421-431 Verrillo, S.C Winters, B.D (2018) Continuous Monitoring to Detect Failure to Rescue in Adult Postoperative Patients Biomedical Instrumentation and Technology, July/August: 281-287 Verrillo, S.C., Cvach, M., Hudson, K.W.,Winters, B.D (2018) Using Continuous Vital Sign Monitoring to Detect Early Deterioration in Adult Postoperative Inpatients Journal of Nursing Care Quality, (published ahead of print – April 2019, Vol 34, Issue 2) Appendix B Timetable for Accomplishing the Work Submit STN application Secure Consultant’s ICA, Biosketch, training Letters of Support Secure JHUSON Letter of Support Submit IRB application Submit Certificates of IRB Training Submit data collection tool Submit staff satisfaction survey Submitpatient satisfaction survey Submit budget Attend staff training in cVSM Send out pre go live staff satisfaction survey Secure biostatistician service schedule Perform daily data collection per protocol – including patient satisfaction surveys Enter data into secure database Receive daily analytics from consultant on vital sign data Perform twice weekly QA/QC on staff performance Send out quarterly staff satisfaction survey Send SPSS data to biostatistician for initial analytical review Pre-Work (Dec 13, 2018 Study Period (Mar 11, Post Study (Mar 11, 2020 – March 10, 2019) 2019 – Mar 10, 2020) – Dec, 31, 2020) X X X X X X X X X X X X X X X X X X X X Review any critical events with coinvestigator, inform IRB and grant funder and make appropriate changes Send quarterly report to grant funder Obtain final analytics from Consultants and biostatistician Submit Final Reports to funders and consultants Submit abstracts for conference presentations and Submit abstract to STN Conference Close study – inform IRB X X X X X X X ViSi Mobile ®System Patient Survey INSTITUTION UNIT _ DATE Survey Instructions: Select he answer that corresponds to your level of disagreement (left) or agreement (right) with each item: strongly disagree, Disagree, Somewhat Disagree, Somewhat Agree, Agree, Strongly Agree Strongly Disagee Disagree Somewhat Disagree Somewhat Agree Agree Strongly Agree I understand the ViSi Mobile monitor Continuously monitors my vital signs And alerts my nurse if there is a change In my health status I feel safer wearing the ViSi mobile Monitor because it continuously Monitors my vital signs The ViSi Mobile monitor was Comfortable to wear The ViSi Mobile monitor did not Restrict my mobility I was able to get more sleep / rest Please provide any additional comments about your experience with the ViSi Mobile monitor Not Applicable VISI Mobile Clinician Survey Dear Colleague, The standard of care for obtaining vital signs has been approved by Johns Hopkins Hospital Administration and Leadership to now use a wireless patient monitoring system, Sotera ViSi Wireless® on the general ward to improve patient safety and quality We are very interested in the opinion of the nurses with regards to this system and are asking the nurses to complete the attached survey of their opinions Your completing the survey is voluntary, anonymous, and gratefully appreciated Participation in the survey is voluntary, and there is no requirement for you to be a participant If you are willing to so, please fill out the enclosed questionnaire, which will tell us about your job, your perceptions of the technology, and whether you think it contributes positively to patient safety The questionnaire will take about 10 minutes to complete When completing the questionnaire, any questions you choose not to answer should be left blank No one at your work place will ever see your answers Our research staff will be the only people to ever see your answers Any reports from this study will use responses from all of the participants so that no one person can be identified No responses of individuals or small groups of individuals will ever be used It is our hope that through the information we obtain from this project we can better understand how the implementation of such technologies affect nurses Thank you for your consideration Sincerely, Sue Carol Verrillo, DNP, RN, CRRN|Assistant Professor Johns Hopkins School of Nursing 525 North Wolfe Street, Rm 453 Baltimore, MD 21205 O.(410)614-2418|E sverril1@jhmi.edu Section A About your job What is your current position _Staff RN; _Float/Agency RN; _Clin Tech; _Support Staff How long have you worked for your current employer _years _months? How long have you worked in your present position _years _months? What shifts you typically work: _Days; _Evenings; _Nights; _Rotate What is your gender: _Female; _Male; _Prefer not to answer What is your education level: _High School/GED; _ADN; _BSN; _MSN; _DNP What is your age range: _less than 30; _30-39; _40-49; _50-59; _60 or greater Section B Survey Instructions: Select the answer that corresponds to your level of disagreement (left) or agreement (right) with each item: Strongly Disagree, Disagree, Somewhat Disagree, Somewhat Agree, Agree, Strongly Agree Please add any comments you wish to share: Appendix G Consent Forms Continuous vital sign monitoring is being adopted as the standard of care and is not considered research at the Johns Hopkins Hospital, Department of Surgery, Zayed 11 East inpatient unit Therefore the patient’s consent to treatment document, that is executed upon admission to the hospital, will suffice as their consent for this modality of obtaining vital signs The research will be the analysis of the dynamic characteristics of the vital signs For this analysis the vital signs will be de-identified and analyzed by an Independent Contractor, who will apply proven logarithms through a relational database to the vital signs, to determine the vital sign alarm pattern for three potentially lethal complications in general ward patients This analysis presents low risk to the patient, as clinical decision making will be happening in real-time independent of the research Appendix H Nurse Residency Program Internal Data Figure Casey-Fink Results (Johns Hopkins Hospital Internal Data)* Areas of evaluation* JHH (n= not included)* UHC Member Academic Medical Centers (n=not included)* Skills: Top most uncomfortable Ventilator Care Ventilator Care Blood Product Administration Blood Product Administration Tracheostomy Care Tracheostomy Care Code Emergency Response Code emergency Response Personal Relationships Finances Student Loans Student Loans Lack of Confidence Lack of confidence Fear of doing harm Fear of doing harm Workload Workload Stress: Top Identified Stressors Transition to RN: Top Concerns Satisfaction: Most satisfying part of work Interacting families with patients and Peer support Making a difference Satisfaction: least satisfying part of work Unrealistic nurse:patient ratios Unrealsitic nurse:patient ratios Work schedule Work schedule Futility of care Futility of care *Retrieved from the Johns Hopkins Adult and Pediatric Nurse Residency Program Annual Report for CY2014, pg Used with permission Budget Justification GRANT FUNDS Personnel Ginger Hanson, PhD - $5,000 (includes salary + fringes) will be allocated toward providing the paid services of a biostatistician at 3% effort I have reviewed the study protocol, the scope of work and the previous preliminary work with the biostatistician The biostatistician understands the scope of work and what biostatistical support services will be needed The biostatistician agreed to the terms of services and the amount potentially being paid, if the grant is awarded Consultants Anthony Lorenzo and Joe Buttafoco - $10,000 This grant application is being submitted in order to gain research monies that will advance the progress of the research project In order to identify the true and consistent continuous vital sign alarm pattern for three potentially lethal complications in general ward patients, the analysts with the most expertise analytic process need to be in place Mr Lorenzo and Mr Buttafoco have demonstrated the validity and reliability of their logarithms in their businesses and will be functioning as Independent Contractors for this research study Now they will be applying their advanced expertise to analyzing de-identified vital signs No one else in the country has their level of expertise and experience We are highly confident that Mr Lorenzo and Mr Buttafoco will be able to adapt their program to analyze the de-identified vital sign data in real time with high accuracy Mr Lorenzo and Mr Buttafoco will each receive $5,000 for the services rendered under the scope of the research project IN-KIND SUPPORT Personnel Sue Carol Verrillo, DNP, RN, CRRN – $33,961, 20% Effort (includes salary + fringes) will be the lead investigator As lead investigator, Dr Verrillo will be responsible for the daily activities of the study protocol, data collection and analysis, human subject protection and dissemination of the study findings Dr Verrillo will ensure that all applicable Johns Hopkins Medicine IRB policies and guidelines are followed during the study period Bradford D Winters, Ph.D., M.D., FCCM - $16,709, 5% Effort (includes salary + fringes) will be the co-Investigator As co-Investigator, Dr Winters will be responsible to act on behalf of Dr Verrillo, in her absence, for the daily activities of the study protocol, data collection and analysis, human subject protection and dissemination of study findings Dr Winters will ensure that all applicable Johns Hopkins Medicine IRB policies and guidelines are followed during the study period Supplies - $12,228 The durable medical supplies are being paid for through Johns Hopkins Hospital (JHH) Central Financing budgeted monies that were awarded for this project to go forward Disposable supplies needed for patient use are being accounted for through the JHH Zayed 11 East unit specific budget Individual patient’s insurances will be charged for single patient use supplies Equipment - $261,339 The durable medical equipment includes Sotera ViSi Wireless® monitors, remote viewing devices, battery chargers, batteries, blood pressure calibration modules are being paid for through Johns Hopkins Hospital Central Financing School of Nursing Indirect Cost - $31,269 Johns Hopkins University’s normal rate of 63.75% has been waived in support of this project Total Matching Funds - $355,506 Principal Investigator/Program Director (Last, First, Middle): Grant Title: Identifying the continuous vital sign alarm patterns for three potentially lethal complications in general ward patients Grant Submission Date: January 31, 2019 Verrillo, Sue, Carol STNRESEARCH GRANT BUDGET WORKSHEET Year Salary % $ Effort Fringe % $ 34 1305 Year (if needed) Salary Year % $ Total Effort Fringe % $ Year Total Total Grant Personnel Ginger Hanson 3,783 Consultant Buttafoco 5,000 Consultant Lorenzo 5,000 Personnel Year Total 5,088 15,088 Year Total Supplies Supplies Year Total Year Total Equipment Equipment Year Total Travel (Travel to conferences for presentations cannot be incorporated into this grant) Year Total Travel Year Total Year Total Software Year Total Year Total Software Other Expenses $88 will be cost shared Other Year Total TOTAL -88 15,000 Year Total Project Total = 15,000 Implementing Continuous Vital Sign Monitoring (cVSM) on the General Ward (Zayed 11 East) Sue Carol Verrillo, DNP, RN, CRRN Assistant Professor Johns Hopkins School of Nursing 525 North Wolfe Street, Rm 453 Baltimore, MD 21205 O.(410)614-2418|E sverril1@jhmi.edu Bradford D Winters, PhD., M.D., FCCM Associate Professor Anesthesiology and Critical Care Medicine/Surgery Division Director, Critical Care Medicine Co-Director, Surgical Intensive Care Unit Core Faculty Armstrong Institute for Patient Quality/Safety Johns Hopkins Hospital 1800 Orleans Street Baltimore, MD 21287 O.(410)955-9080|E bwinters@jhmi.edu Background and Significance National Data ã HealthGrades 2013ạhas reported that every out of 10 postoperative Medicare patients currently dies after developing: • PE/DVT • Pneumonia/Sepsis • Shock/Cardiac Arrest • Occult GIB ¹ Reed, K., May, R., Nicholas, C (Eds) (2013) HealthGrades Patient Safety in American Hospitals Study Denver: Health Grades, Inc Local Data • • • • • In FY15 Zayed 11 East had failure to rescue deaths JH Finance analysis revealed: cVSM could reduce MD Hospital Acquired Conditions (MHACs) by 30% ($375K)and associated legal claims/settlements by $2.5K (internal data) Medicare patients comprise 42% of total charge reduction (internal data) Trauma patients comprised 36% of Zayed 11 East population (internal data) Synthesis of Literature No single vital sign parameter can predict all types of deterioration: all parameters are needed to detect all the possible types of deterioration (Taenzer, Pyke, McGrath, 2011) Undetected deterioration from cardiopulmonary events such as pulmonary embolisms/ deep vein thrombosis, new onset arrhythmias, postoperative myocardial infarctions or strokes need earlier detection (Lynn & Curry, 2011) Literature supports the use of cVSM due to the high risk of failure to rescue death from undiagnosed obstructive sleep apnea in the presence of anesthesia and PCA pumps (Kaw et al, 2012) The majority of unplanned transfers to an ICU happened within the first 48 hours of ward admission- with the highest risk for postoperative complications in the first 24 hours ( Liu, Kipnis, Rizk, Escobar, 2012) Failure to rescue deaths from carbon dioxide narcosis can happen in any patient from over using PCA pumps ( Maddox et al, 2008) Table Pre-Intervention and Intervention Group Key Failure to Rescue Outcome Measures *p