Effect of an ML Electronic Alert Management System to Reduce the Use of ED Visits and Hospitalizations

Emergencies Clinical Trial

Official title:

Effect of an Electronic Alert Management System Using Caregivers' Observations and Machine Learning Algorithm to Reduce the Use of Emergency Department Visits and Unplanned Hospitalizations Among Older People

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05221697
• Other study ID #: PRESAGE_2021-01
• Status: Active, not recruiting
• Phase: N/A
• Start date: September 1, 2020
• Est. completion date: June 30, 2024

Study information

• Verified date: August 2023
• Source: Presage
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Development, validation and impact of an alert management system using social workers' observations and machine learning algorithms to predict 7-to-14-day alerts for the risk of Emergency Department (ED) Visit and unplanned hospitalization.

Multi-center trial implementation of electronic Home Care Aides-reported outcomes measure system among patients, frail adults >= 65 years living at home and receiving assistance from home care aides (HCA).

Description:

On a weekly basis, after home visit, HCAs reported on participants' functional status using a smartphone application that recorded 23 functional items about each participant (e.g., ability to stand, move, eat, mood, loneliness). Predictive system using Machine learning techniques (i.e., leveraging random forest predictors) was developed and generated 7 to 14-day predictive alerts for the risk of ED visit to nurses.

This questionnaire focused on functional and clinical autonomy (ie, activities of daily life), possible medical symptoms (eg, fatigue, falls, and pain), changes in behavior (eg, recognition and aggressiveness), and communication with the HA or their surroundings. This questionnaire is composed of very simple and easy-to-understand questions, giving a global view of the person's condition. For each of the 23 questions, a yes/no answer was requested. Data recorded by HAs were sent in real time to a secure server to be analyzed by our machine learning algorithm, which predicted the risk level and displayed it on a web-based secure medical device called PRESAGE CARE, which is CE marked. Particularly, when the algorithm predicted a high-risk level, an alert was displayed in the form of a notification on the screen to the coordinating nurse of the health care network center of the district. This risk notification was accompanied by information about recent changes in the patients' functional status, identified from the HAs' records, to assist the coordinating nurse in interacting with family caregiver and other health professionals.

In the event of an alert, the coordinating nurse called the family caregiver to inquire about recent changes in the patient's health condition and for doubt removal and could then decide to ask for a health intervention according to a health intervention model developed before the start of the study. In brief, this alert-triggered health intervention (ATHI) consisted of calling the patient's nurse (if the patient had regular home visits of a nurse) or the patient's general practitioner and informing them of a worsening of the patient's functional status and a potential risk of an ED visit or unplanned hospitalization in the next few days according to the eHealth system algorithm. This model of ATHI had been presented and approved by the Agences Régionales de Santé of the regions involved in our study

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 800
• Est. completion date: June 30, 2024
• Est. primary completion date: December 31, 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 75 Years and older

Eligibility

Inclusion Criteria:

• age of 75 yo mini

• receiving the help of a social worker

• patient should give their consent

• patient should had seen their primary care professional within the past 12 months

Exclusion Criteria:

• People with severe dependence (French national instrument, which stratifies dependency level from group iso-resources (GIR) : 1 (very severe dependency) and 2 (severe dependency)

Related Conditions & MeSH terms

• Emergencies

Intervention

Device:
• PRESAGE CARE
Participants in this arm will be followed by HCA and might benefit from Nurse health interventions

Locations

Country	Name	City	State
France	Grand Versailles	Le Chesnay
France	Marseille-1	Marseille

Sponsors (4)

Lead Sponsor	Collaborator
Presage	Assistance Publique - Hôpitaux de Paris, Assistance Publique Hopitaux De Marseille, University Hospital, Lille

Country where clinical trial is conducted

France, 

References & Publications (32)

Adam R, Burton CD, Bond CM, de Bruin M, Murchie P. Can patient-reported measurements of pain be used to improve cancer pain management? A systematic review and meta-analysis. BMJ Support Palliat Care. 2017 Dec;7(4):0. doi: 10.1136/bmjspcare-2016-001137. Epub 2016 Nov 22. — View Citation

Adler-Milstein J, Chen JH, Dhaliwal G. Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to "Wayfinding". JAMA. 2021 Dec 28;326(24):2467-2468. doi: 10.1001/jama.2021.22396. No abstract available. — View Citation

Anderson KO, Palos GR, Mendoza TR, Cleeland CS, Liao KP, Fisch MJ, Garcia-Gonzalez A, Rieber AG, Nazario LA, Valero V, Hahn KM, Person CL, Payne R. Automated pain intervention for underserved minority women with breast cancer. Cancer. 2015 Jun 1;121(11):1882-90. doi: 10.1002/cncr.29204. Epub 2015 Feb 24. — View Citation

Barrachina-Fernandez M, Maitin AM, Sanchez-Avila C, Romero JP. Wearable Technology to Detect Motor Fluctuations in Parkinson's Disease Patients: Current State and Challenges. Sensors (Basel). 2021 Jun 18;21(12):4188. doi: 10.3390/s21124188. — View Citation

Beaudouin, Valérie and Bloch, Isabelle and Bounie, David and Bounie, David and Clémençon, Stéphan and d'Alché-Buc, Florence and Eagan, James and Maxwell, Winston and Mozharovskyi, Pavlo and Parekh, Jayneel, Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach (March 23, 2020). Available at SSRN: https://ssrn.com/abstract=3559477 or http://dx.doi.org/10.2139/ssrn.3559477

Belmin J, Auffray JC, Berbezier C, Boirin P, Mercier S, de Reviers B, Golmard JL. Level of dependency: a simple marker associated with mortality during the 2003 heatwave among French dependent elderly people living in the community or in institutions. Age Ageing. 2007 May;36(3):298-303. doi: 10.1093/ageing/afm026. Epub 2007 Mar 24. — View Citation

Bouazza YB, Chiairi I, El Kharbouchi O, De Backer L, Vanhoutte G, Janssens A, Van Meerbeeck JP. Patient-reported outcome measures (PROMs) in the management of lung cancer: A systematic review. Lung Cancer. 2017 Nov;113:140-151. doi: 10.1016/j.lungcan.2017.09.011. Epub 2017 Sep 23. — View Citation

Camargo CA Jr, Tsai CL, Sullivan AF, Cleary PD, Gordon JA, Guadagnoli E, Kaushal R, Magid DJ, Rao SR, Blumenthal D. Safety climate and medical errors in 62 US emergency departments. Ann Emerg Med. 2012 Nov;60(5):555-563.e20. doi: 10.1016/j.annemergmed.2012.02.018. — View Citation

Chen CC, Wang C, Huang GH. Functional trajectory 6 months posthospitalization: a cohort study of older hospitalized patients in Taiwan. Nurs Res. 2008 Mar-Apr;57(2):93-100. doi: 10.1097/01.NNR.0000313485.18670.e2. — View Citation

Clerencia-Sierra M, Calderon-Larranaga A, Martinez-Velilla N, Vergara-Mitxeltorena I, Aldaz-Herce P, Poblador-Plou B, Machon-Sobrado M, Egues-Olazabal N, Abellan-van Kan G, Prados-Torres A. Multimorbidity Patterns in Hospitalized Older Patients: Associations among Chronic Diseases and Geriatric Syndromes. PLoS One. 2015 Jul 24;10(7):e0132909. doi: 10.1371/journal.pone.0132909. eCollection 2015. — View Citation

Crane SJ, Tung EE, Hanson GJ, Cha S, Chaudhry R, Takahashi PY. Use of an electronic administrative database to identify older community dwelling adults at high-risk for hospitalization or emergency department visits: the elders risk assessment index. BMC Health Serv Res. 2010 Dec 13;10:338. doi: 10.1186/1472-6963-10-338. — View Citation

Denis F, Krakowski I. How Should Oncologists Choose an Electronic Patient-Reported Outcome System for Remote Monitoring of Patients With Cancer? J Med Internet Res. 2021 Sep 9;23(9):e30549. doi: 10.2196/30549. — View Citation

Duncanson E, Bennett PN, Viecelli A, Dansie K, Handke W, Tong A, Palmer S, Jesudason S, McDonald SP, Morton RL; Symptom monitoring WIth Feedback Trial (SWIFT) Investigators. Feasibility and acceptability of e-PROMs data capture and feedback among patients receiving haemodialysis in the Symptom monitoring WIth Feedback Trial (SWIFT) pilot: protocol for a qualitative study in Australia. BMJ Open. 2020 Nov 6;10(11):e039014. doi: 10.1136/bmjopen-2020-039014. — View Citation

Gasperini B, Cherubini A, Pierri F, Barbadoro P, Fedecostante M, Prospero E. Potentially preventable visits to the emergency department in older adults: Results from a national survey in Italy. PLoS One. 2017 Dec 21;12(12):e0189925. doi: 10.1371/journal.pone.0189925. eCollection 2017. — View Citation

Gray JT, Walker A. Avoiding admissions from the ambulance service: a review of elderly patients with falls and patients with breathing difficulties seen by emergency care practitioners in South Yorkshire. Emerg Med J. 2008 Mar;25(3):168-71. doi: 10.1136/emj.2007.050732. — View Citation

Hu Z, Jin B, Shin AY, Zhu C, Zhao Y, Hao S, Zheng L, Fu C, Wen Q, Ji J, Li Z, Wang Y, Zheng X, Dai D, Culver DS, Alfreds ST, Rogow T, Stearns F, Sylvester KG, Widen E, Ling XB. Real-time web-based assessment of total population risk of future emergency department utilization: statewide prospective active case finding study. Interact J Med Res. 2015 Jan 13;4(1):e2. doi: 10.2196/ijmr.4022. — View Citation

Huntley AL, Chalder M, Shaw ARG, Hollingworth W, Metcalfe C, Benger JR, Purdy S. A systematic review to identify and assess the effectiveness of alternatives for people over the age of 65 who are at risk of potentially avoidable hospital admission. BMJ Open. 2017 Aug 1;7(7):e016236. doi: 10.1136/bmjopen-2017-016236. — View Citation

Inglis SC, Clark RA, McAlister FA, Ball J, Lewinter C, Cullington D, Stewart S, Cleland JG. Structured telephone support or telemonitoring programmes for patients with chronic heart failure. Cochrane Database Syst Rev. 2010 Aug 4;(8):CD007228. doi: 10.1002/14651858.CD007228.pub2. — View Citation

Iwashyna TJ, Ely EW, Smith DM, Langa KM. Long-term cognitive impairment and functional disability among survivors of severe sepsis. JAMA. 2010 Oct 27;304(16):1787-94. doi: 10.1001/jama.2010.1553. — View Citation

Kahn JH, Magauran BG Jr, Olshaker JS, Shankar KN. Current Trends in Geriatric Emergency Medicine. Emerg Med Clin North Am. 2016 Aug;34(3):435-52. doi: 10.1016/j.emc.2016.04.014. — View Citation

Khalil H, Bell B, Chambers H, Sheikh A, Avery AJ. Professional, structural and organisational interventions in primary care for reducing medication errors. Cochrane Database Syst Rev. 2017 Oct 4;10(10):CD003942. doi: 10.1002/14651858.CD003942.pub3. — View Citation

Mason S, Knowles E, Colwell B, Dixon S, Wardrope J, Gorringe R, Snooks H, Perrin J, Nicholl J. Effectiveness of paramedic practitioners in attending 999 calls from elderly people in the community: cluster randomised controlled trial. BMJ. 2007 Nov 3;335(7626):919. doi: 10.1136/bmj.39343.649097.55. Epub 2007 Oct 4. — View Citation

Mason S, O'Keeffe C, Knowles E, Bradburn M, Campbell M, Coleman P, Stride C, O'Hara R, Rick J, Patterson M. A pragmatic quasi-experimental multi-site community intervention trial evaluating the impact of Emergency Care Practitioners in different UK health settings on patient pathways (NEECaP Trial). Emerg Med J. 2012 Jan;29(1):47-53. doi: 10.1136/emj.2010.103572. — View Citation

McCusker J, Verdon J. Do geriatric interventions reduce emergency department visits? A systematic review. J Gerontol A Biol Sci Med Sci. 2006 Jan;61(1):53-62. doi: 10.1093/gerona/61.1.53. — View Citation

Mooney KH, Beck SL, Friedman RH, Farzanfar R, Wong B. Automated monitoring of symptoms during ambulatory chemotherapy and oncology providers' use of the information: a randomized controlled clinical trial. Support Care Cancer. 2014 Sep;22(9):2343-50. doi: 10.1007/s00520-014-2216-1. Epub 2014 Apr 1. — View Citation

O'Connell S, Palmer R, Withers K, Saha N, Puntoni S, Carolan-Rees G; PROMs, PREMs and Effectiveness Programme. Requirements for the collection of electronic PROMS either "in clinic" or "at home" as part of the PROMs, PREMs and Effectiveness Programme (PPEP) in Wales: a feasibility study using a generic PROM tool. Pilot Feasibility Stud. 2018 Jul 4;4:90. doi: 10.1186/s40814-018-0282-8. eCollection 2018. — View Citation

Schick-Makaroff K, Karimi-Dehkordi M, Cuthbertson L, Dixon D, Cohen SR, Hilliard N, Sawatzky R. Using Patient- and Family-Reported Outcome and Experience Measures Across Transitions of Care for Frail Older Adults Living at Home: A Meta-Narrative Synthesis. Gerontologist. 2021 Apr 3;61(3):e23-e38. doi: 10.1093/geront/gnz162. — View Citation

Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Furstenau D, Biessmann F, Wolf-Ostermann K. Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review. J Med Internet Res. 2021 Nov 29;23(11):e26522. doi: 10.2196/26522. — View Citation

Soril LJ, Leggett LE, Lorenzetti DL, Noseworthy TW, Clement FM. Reducing frequent visits to the emergency department: a systematic review of interventions. PLoS One. 2015 Apr 13;10(4):e0123660. doi: 10.1371/journal.pone.0123660. eCollection 2015. — View Citation

Takahashi PY, Heien HC, Sangaralingham LR, Shah ND, Naessens JM. Enhanced risk prediction model for emergency department use and hospitalizations in patients in a primary care medical home. Am J Manag Care. 2016 Jul;22(7):475-83. — View Citation

Vasey B, Ursprung S, Beddoe B, Taylor EH, Marlow N, Bilbro N, Watkinson P, McCulloch P. Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review. JAMA Netw Open. 2021 Mar 1;4(3):e211276. doi: 10.1001/jamanetworkopen.2021.1276. — View Citation

Veyron JH, Friocourt P, Jeanjean O, Luquel L, Bonifas N, Denis F, Belmin J. Home care aides' observations and machine learning algorithms for the prediction of visits to emergency departments by older community-dwelling individuals receiving home care assistance: A proof of concept study. PLoS One. 2019 Aug 13;14(8):e0220002. doi: 10.1371/journal.pone.0220002. eCollection 2019. — View Citation

* Note: There are 32 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Unplanned Hospitalization rate	Comparison between unplanned hospitalization ratio from 2 randomized groups (intervention and control arms).; P values <.05 will be considered statistically significant.	through study completion, an average of 1 year
Primary	Event-free survival (EFS)	Comparison average Time for first adverse event between intervention and control groups.; P values <.05 will be considered statistically significant.	through study completion, an average of 1 year
Primary	Impact on older adults and relatives' quality of life (European Quality of Life 5 Dimensions and 3 Lines scale)	Comparison of the average score of EQ5D-3L quality of life scale (European Quality of Life 5 Dimensions and 3 Lines) between intervention and control groups.; P values <.05 will be considered statistically significant.	through study completion, an average of 1 year
Primary	Cost-effectiveness	Incremental cost-effectiveness ratio (ICER), QALY. Willingness-to-pay thresholds of €30,000 per quality-adjusted life year (QALY) and €90,000 per QALY were used to define a very cost-effective and cost-effective strategy, respectively	through study completion, an average of 1 year
Secondary	Impact on users : time needed to complete questionnaire	Time needed to complete questionnaire (minutes) : a time of less than 2 minutes will be considered acceptable	through study completion, an average of 1 year
Secondary	Intervention rate	Part of alert which leads to interventions and intervention time (%). Rate of over 70% is considered acceptable.	through study completion, an average of 1 year
Secondary	Intervention time	Mean of the duration between day of alert and day of intervention (in days). A delay of less than 4 days is considered acceptable.	through study completion, an average of 1 year
Secondary	Time needed to analysis patient statut	Time needed to analysis patient statut (hours and minutes) : a time of less than 15 minutes by patient will be considered acceptable	through study completion, an average of 1 year
Secondary	Impact on quality of care	Positive or very positive impact on quality of care : rate of over 80% is considered acceptable.	through study completion, an average of 1 year
Secondary	Impact on Professional' Relationship and coordination	Positive or very positive impact on professionnal relationship and coordination :rate of over 80% is considered acceptable.	through study completion, an average of 1 year