Klinik - Intelligent Patient Flow Management

NCT04577079 | Completed

• 1 other identifier: KUH507P004
• Study Type: observational
• Target: 273
• Locations: 1 country
• Sites: 1

Brief Summary

Digital health technologies (DHT) are increasingly developed to support healthcare systems around the world. However, they are frequently lacking evidence-based medicine and medical validation. There is considerable need in the western countries to allocate healthcare resources accurately and give the population detailed and reliable health information enabling to take greater responsibility for their health. Intelligent patient flow management system (IPFM, product name Klinik Frontline) is developed to meet these needs. In practice, IPFM is used for decision support in the triaging and diagnostic processes as well as automatizing the management of inflow of the patients. The core of the IPFM is a clinical artificial intelligence (AI), which utilizes a comprehensive medical database of clinical correlations generated by medical doctors. The study population of this research consists of patients from the Emergency Department of Kuopio University Hospital (KUH). Data will be gathered during 2 weeks of piloting, after which the results will be analysed. Anticipated number of patients to the study is minimum of 246 patients, with objective to be several hundreds. When attending to the hospital, patients will report their demographics, background information and symptoms using structured IPFM online form. Patients entering the unit in an ambulance or with need of immediate care of healthcare professionals due to severe and acute conditions are referred similar to normal process to ensure the patient safety. Results obtained from IPFM are blinded from the healthcare professional and IPFM does not affect professional's clinical decision making in any way. The data obtained from IPFM online form and clinical data from the emergency department and KUH will be analysed after the data collection. The main aim of the research is to validate the use of IPFM by evaluating the association of IPFM output with 1) urgency and severity of the conditions (using Emergency Severity Index \[ESI\], an international triaging protocol for emergency units, and an assessment by triage nurse); and 2) actual diagnoses diagnosed by medical doctors. The main hypotheses of the research are that 1) IPFM is safe and sensitive in evaluating the urgency of the conditions of arriving patients at the emergency department and that 2) IPFM has sufficient correlation of differential diagnosis with actual diagnosis made by medical doctor.

Conditions

Emergency Medical Services

Keywords

Intelligent patient flow management

Outcome Measures

Primary Outcomes (2)

• Specificity (%) of intelligent patient flow management (IPFM) correlated with the evaluation of trained emergency (triage) nurse.

  The number of missed emergency cases evaluated by IPFM.

  Through study completion, estimated until the end of 2020.

• Sensitivity (%) of intelligent patient flow management (IPFM) correlated with the evaluation of trained emergency (triage) nurse.

  The number of correct emergency severity index (ESI) class

  Through study completion, estimated until the end of 2020.

Secondary Outcomes (1)

• Correlation (%) of differential diagnosis

  Through study completion, estimated until the end of 2020.

Study Arms (1)

IPMF Screened

Patient screened and assessed by intelligent patient flow management system.

Device: Evaluation of the need of emergency medical services

Interventions

Evaluation of the need of emergency medical services DEVICE

The main aim of this study is to validate the use of IPFM in a hospital setting by evaluating the association of IPFM output with 1) clinical urgency and severity of the conditions (using Emergency Severity Index \[ESI\], an international triaging protocol for emergency units, and an assessment by triage nurse); and 2) actual diagnoses made by the hospital doctors. The objective is also to assess the correlation of IPFM output with redirection or referral to various specialties

IPMF Screened

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients living in the district of North-Savo (250 000).

You may qualify if:

• All adult (\>18 years of age) patients independently (walking) entering emergency care ward with written consent.

You may not qualify if:

• Patients arriving with ambulance
• Patients needing immediate care
• Patients under 18 years of age
• Patients with restricted capabilities or developmental disorders.

Sponsors & Collaborators

• Kuopio University Hospital: lead

Study Sites (1)

Kuopio university hospital

Kuopio, Eastern-Finland, 70029, Finland

Location

Related Publications (7)

• Greaves F, Joshi I, Campbell M, Roberts S, Patel N, Powell J. What is an appropriate level of evidence for a digital health intervention? Lancet. 2019 Dec 22;392(10165):2665-2667. doi: 10.1016/S0140-6736(18)33129-5. Epub 2018 Dec 10. No abstract available.

  PMID: 30545779 BACKGROUND

• The Lancet. Is digital medicine different? Lancet. 2018 Jul 14;392(10142):95. doi: 10.1016/S0140-6736(18)31562-9. No abstract available.

  PMID: 30017135 BACKGROUND

• Fraser H, Coiera E, Wong D. Safety of patient-facing digital symptom checkers. Lancet. 2018 Nov 24;392(10161):2263-2264. doi: 10.1016/S0140-6736(18)32819-8. Epub 2018 Nov 6. No abstract available.

  PMID: 30413281 BACKGROUND

• Singh H, Meyer AN, Thomas EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf. 2014 Sep;23(9):727-31. doi: 10.1136/bmjqs-2013-002627. Epub 2014 Apr 17.

  PMID: 24742777 BACKGROUND

• Singh H, Giardina TD, Meyer AN, Forjuoh SN, Reis MD, Thomas EJ. Types and origins of diagnostic errors in primary care settings. JAMA Intern Med. 2013 Mar 25;173(6):418-25. doi: 10.1001/jamainternmed.2013.2777.

  PMID: 23440149 BACKGROUND

• Elias P, Damle A, Casale M, Branson K, Churi C, Komatireddy R, Feramisco J. A Web-Based Tool for Patient Triage in Emergency Department Settings: Validation Using the Emergency Severity Index. JMIR Med Inform. 2015 Jun 10;3(2):e23. doi: 10.2196/medinform.3508.

  PMID: 26063343 BACKGROUND

• Tenhunen H, Hirvonen P, Linna M, Halminen O, Horhammer I. Intelligent Patient Flow Management System at a Primary Healthcare Center - The Effect on Service Use and Costs. Stud Health Technol Inform. 2018;255:142-146.

  PMID: 30306924 BACKGROUND

Study Officials

• Tero J Martikainen, MD. PhD

  Kuopion University Hospital, Emergency medicine

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Head physician of Emergency Care Department

Study Record Dates

• First Submitted: September 3, 2020
• First Posted: October 6, 2020
• Study Start: September 1, 2020
• Primary Completion: October 31, 2020
• Study Completion: November 18, 2022
• Last Updated: November 21, 2022

Record last verified: 2022-11

Data Sharing

• IPD Sharing: Will not share

Locations

FI (1)