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Appl Clin Inform 2021; 12(05): 1161-1173
DOI: 10.1055/s-0041-1740480
Special Section on Workflow Automation

Continuous Remote Patient Monitoring: Evaluation of the Heart Failure Cascade Soft Launch

Wei Ning Chi
1   Outcomes Research Network, NorthShore University HealthSystem, Evanston, Illinois, United States
,
Courtney Reamer
2   Department of Medicine, NorthShore University HealthSystem, Evanston, Illinois, United States
,
Robert Gordon
2   Department of Medicine, NorthShore University HealthSystem, Evanston, Illinois, United States
,
Nitasha Sarswat
2   Department of Medicine, NorthShore University HealthSystem, Evanston, Illinois, United States
3   Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, Illinois, United States
,
Charu Gupta
2   Department of Medicine, NorthShore University HealthSystem, Evanston, Illinois, United States
,
Emily White VanGompel
4   Department of Family Medicine, NorthShore University HealthSystem, Evanston, Illinois, United States
5   Department of Family Medicine, University of Chicago Pritzker School of Medicine, Chicago, Illinois, United States
,
Julie Dayiantis
6   Home and Hospice Services, NorthShore University HealthSystem, Evanston, Illinois, United States
,
Melissa Morton-Jost
6   Home and Hospice Services, NorthShore University HealthSystem, Evanston, Illinois, United States
,
Urmila Ravichandran
7   Health Information Technology, NorthShore University HealthSystem, Evanston, Illinois, United States
,
Karen Larimer
8   Clinical Department, physIQ, Inc., Chicago, Illinois, United States
,
David Victorson
9   Northwestern University Feinberg School of Medicine, Evanston, Illinois, United States
,
John Erwin
2   Department of Medicine, NorthShore University HealthSystem, Evanston, Illinois, United States
3   Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, Illinois, United States
,
Lakshmi Halasyamani
4   Department of Family Medicine, NorthShore University HealthSystem, Evanston, Illinois, United States
5   Department of Family Medicine, University of Chicago Pritzker School of Medicine, Chicago, Illinois, United States
,
Anthony Solomonides
1   Outcomes Research Network, NorthShore University HealthSystem, Evanston, Illinois, United States
,
Rema Padman
10   The Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
,
Nirav S. Shah
2   Department of Medicine, NorthShore University HealthSystem, Evanston, Illinois, United States
3   Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, Illinois, United States
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Abstract

Objective We report on our experience of deploying a continuous remote patient monitoring (CRPM) study soft launch with structured cascading and escalation pathways on heart failure (HF) patients post-discharge. The lessons learned from the soft launch are used to modify and fine-tune the workflow process and study protocol.

Methods This soft launch was conducted at NorthShore University HealthSystem's Evanston Hospital from December 2020 to March 2021. Patients were provided with non-invasive wearable biosensors that continuously collect ambulatory physiological data, and a study phone that collects patient-reported outcomes. The physiological data are analyzed by machine learning algorithms, potentially identifying physiological perturbation in HF patients. Alerts from this algorithm may be cascaded with other patient status data to inform home health nurses' (HHNs') management via a structured protocol. HHNs review the monitoring platform daily. If the patient's status meets specific criteria, HHNs perform assessments and escalate patient cases to the HF team for further guidance on early intervention.

Results We enrolled five patients into the soft launch. Four participants adhered to study activities. Two out of five patients were readmitted, one due to HF, one due to infection. Observed miscommunication and protocol gaps were noted for protocol amendment. The study team adopted an organizational development method from change management theory to reconfigure the study protocol.

Conclusion We sought to automate the monitoring aspects of post-discharge care by aligning a new technology that generates streaming data from a wearable device with a complex, multi-provider workflow into a novel protocol using iterative design, implementation, and evaluation methods to monitor post-discharge HF patients. CRPM with structured escalation and telemonitoring protocol shows potential to maintain patients in their home environment and reduce HF-related readmissions. Our results suggest that further education to engage and empower frontline workers using advanced technology is essential to scale up the approach.

Keywords

heart failure - process improvement - multivariate change index - home health nurse - communications


Publication History

Received: 14 June 2021

Accepted: 22 October 2021

Article published online:
29 December 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Brahmbhatt DH, Cowie MR. Remote management of heart failure: an overview of telemonitoring technologies. Card Fail Rev 2019; 5 (02) 86-92
    CrossrefPubMedGoogle Scholar
  • 2 Bekfani T, Fudim M, Cleland JGF. et al. A current and future outlook on upcoming technologies in remote monitoring of patients with heart failure. Eur J Heart Fail 2021; 23 (01) 175-185
    CrossrefPubMedGoogle Scholar
  • 3 Virani SS, Alonso A, Benjamin EJ. et al; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics-2020 update: a report from the American Heart Association. Circulation 2020; 141 (09) e139-e596
    CrossrefPubMedGoogle Scholar
  • 4 Echouffo-Tcheugui JB, Bishu KG, Fonarow GC, Egede LE. Trends in health care expenditure among US adults with heart failure: the medical expenditure panel survey 2002-2011. Am Heart J 2017; 186: 63-72
    CrossrefPubMedGoogle Scholar
  • 5 Krumholz HM, Merrill AR, Schone EM. et al. Patterns of hospital performance in acute myocardial infarction and heart failure 30-day mortality and readmission. Circ Cardiovasc Qual Outcomes 2009; 2 (05) 407-413
    CrossrefPubMedGoogle Scholar
  • 6 Hines AL, Barrett ML, Jiang HJ, Steiner CA. Conditions with the largest number of adult hospital readmissions by payer, 2011, Statistical Brief #172. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Accessed June 4, 2020 at: https://www.hcup-us.ahrq.gov/reports/statbriefs/sb172-Conditions-Readmissions-Payer.jsp
    PubMedGoogle Scholar
  • 7 Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 2009; 360 (14) 1418-1428
    CrossrefPubMedGoogle Scholar
  • 8 Carey K, Stefos T. The cost of hospital readmissions: evidence from the VA. Health Care Manage Sci 2016; 19 (03) 241-248
    CrossrefPubMedGoogle Scholar
  • 9 Lahewala S, Arora S, Tripathi B. et al. Heart failure: Same-hospital vs. different-hospital readmission outcomes. Int J Cardiol 2019; 278: 186-191
    CrossrefPubMedGoogle Scholar
  • 10 Centers for Medicare and Medicaid Services. Hospital Readmissions Reduction Program (HRRP). August 24, 2020. Accessed May 5, 2021: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program
    PubMedGoogle Scholar
  • 11 Veenis JF, Brugts JJ. Remote monitoring for better management of LVAD patients: the potential benefits of CardioMEMS. Gen Thorac Cardiovasc Surg 2020; 68 (03) 209-218
    CrossrefPubMedGoogle Scholar
  • 12 Joshi R, Nair A. The utility of CardioMEMS, a wireless hemodynamic monitoring system in reducing heart failure related hospital readmissions. J Nurse Pract 2021; 17 (03) 267-272
    CrossrefPubMedGoogle Scholar
  • 13 Ding H, Jayasena R, Maiorana A. et al. Innovative Telemonitoring Enhanced Care Programme for Chronic Heart Failure (ITEC-CHF) to improve guideline compliance and collaborative care: protocol of a multicentre randomised controlled trial. BMJ Open 2017; 7 (10) e017550
    CrossrefPubMedGoogle Scholar
  • 14 Ware P, Ross HJ, Cafazzo JA, Boodoo C, Munnery M, Seto E. Outcomes of a heart failure telemonitoring program implemented as the standard of care in an outpatient heart function clinic: pretest-posttest pragmatic study. J Med Internet Res 2020; 22 (02) e16538
    CrossrefPubMedGoogle Scholar
  • 15 Kraai I, de Vries A, Vermeulen K. et al. The value of telemonitoring and ICT-guided disease management in heart failure: results from the IN TOUCH study. Int J Med Inform 2016; 85 (01) 53-60
    CrossrefPubMedGoogle Scholar
  • 16 Ong MK, Romano PS, Edgington S. et al; Better Effectiveness After Transition–Heart Failure (BEAT-HF) Research Group. Effectiveness of remote patient monitoring after discharge of hospitalized patients with heart failure: the better effectiveness after transition—Heart Failure (BEAT-HF) Randomized Clinical Trial. JAMA Intern Med 2016; 176 (03) 310-318
    CrossrefPubMedGoogle Scholar
  • 17 Park C, Otobo E, Ullman J. et al. Impact on readmission reduction among heart failure patients using digital health monitoring: feasibility and adoptability study. JMIR Med Inform 2019; 7 (04) e13353
    CrossrefPubMedGoogle Scholar
  • 18 Olivari Z, Giacomelli S, Gubian L. et al. The effectiveness of remote monitoring of elderly patients after hospitalisation for heart failure: the renewing health European project. Int J Cardiol 2018; 257: 137-142
    CrossrefPubMedGoogle Scholar
  • 19 Ammenwerth E, Modre-Osprian R, Fetz B. et al. Herzmobil, an integrated and collaborative telemonitoring-based disease management program for patients with heart failure: a feasibility study paving the way to routine care. JMIR Cardio 2018; 2 (01) e11
    CrossrefPubMedGoogle Scholar
  • 20 Negarandeh R, Zolfaghari M, Bashi N, Kiarsi M. Evaluating the effect of monitoring through telephone (tele-monitoring) on self-care behaviors and readmission of patients with heart failure after discharge. Appl Clin Inform 2019; 10 (02) 261-268
    Article in Thieme ConnectPubMedGoogle Scholar
  • 21 Zheng K, Padman R, Johnson MP, Diamond HS. Understanding technology adoption in clinical care: clinician adoption behavior of a point-of-care reminder system. Int J Med Inform 2005; 74 (7-8): 535-543
    CrossrefPubMedGoogle Scholar
  • 22 Jacob C, Sanchez-Vazquez A, Ivory C. Understanding clinicians' adoption of mobile health tools: a qualitative review of the most used frameworks. JMIR Mhealth Uhealth 2020; 8 (07) e18072
    CrossrefPubMedGoogle Scholar
  • 23 Stehlik J, Schmalfuss C, Bozkurt B. et al. Continuous wearable monitoring analytics predict heart failure hospitalization: the LINK-HF multicenter study. Circ Heart Fail 2020; 13 (03) e006513
    CrossrefPubMedGoogle Scholar
  • 24 Shah N, Konchak C, Chertok D. et al. Clinical Analytics Prediction Engine (CAPE): development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models. PLoS One 2020; 15 (08) e0238065
    CrossrefPubMedGoogle Scholar
  • 25 Yancy CW, Jessup M, Bozkurt B. et al. 2017 ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation 2017; 136 (06) e137-e161
    CrossrefPubMedGoogle Scholar
  • 26 Yancy CW, Jessup M, Bozkurt B. et al; WRITING COMMITTEE MEMBERS, American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation 2013; 128 (16) e240-e327
    PubMedGoogle Scholar
  • 27 ENRICHD Investigators. Enhancing Recovery in Coronary Heart Disease (ENRICHD) study intervention: rationale and design. Psychosom Med 2001; 63 (05) 747-755
    PubMedGoogle Scholar
  • 28 Sevilla-Cazes J, Ahmad FS, Bowles KH. et al. Heart failure home management challenges and reasons for readmission: a qualitative study to understand the patient's perspective. J Gen Intern Med 2018; 33 (10) 1700-1707
    CrossrefPubMedGoogle Scholar
  • 29 Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. J Am Med Inform Assoc 2020; 27 (03) 491-497
    CrossrefPubMedGoogle Scholar
  • 30 Brodwin E, Ross C. Chapter 3: The emerging science of implementing AI. In: Gil G. Promise and Peril: How Artificial Intelligence is Transforming Health Care. Stat 2020; 70-79
    PubMedGoogle Scholar