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© Borgis - Postępy Nauk Medycznych 6/2018, s. 361-365 | DOI: 10.25121/PNM.2018.31.6.361
*Natasza Blek1, Lukasz Szarpak2, Michalina Drejza3
The use of digital technologies in stroke management in the world: an analysis of examples
Technologie cyfrowe wykorzystywane w opiece nad pacjentami z udarami – analiza przypadków na świecie
1Institute of Neuroscience and Cybernetic Medicine, Faculty of Medicine, Lazarski University, Warsaw, Poland
2Lazarski University, Warsaw, Poland
3Reproductive And Sexual Health Research student, London School of Hygiene And Tropical Medicine, London, United Kingdom
Streszczenie
Celem tej publikacji jest zilustrowanie realistycznego potencjału technologii cyfrowych – aplikacji mobilnych, telemedycyny, zautomatyzowanych systemów analitycznych stosowanych w kilku kluczowych elementach zapobiegania i terapii udaru. Na podstawie danych Światowej Organizacji Zdrowia szacuje się, że udar był przyczyną 5,78 miliona zgonów na świecie w 2016 roku.
Baza PubMed została przeszukana pod kątem stosowanych metod cyfrowych w zapobieganiu i terapii udaru. Po wstępnym przeszukaniu identyfikowano kierunki dalszych poszukiwań w oparciu o najpopularniejsze słowa kluczowe odnoszące się do technologii cyfrowych.
Coraz więcej dowodów naukowych przemawia za skutecznością wykorzystania cyfrowych technologii w opiece nad pacjentem z udarem. Niestety, większość związanych z ucyfrowieniem opieki rekomendacji zawartych jest w wytycznych tworzonych przez towarzystwa amerykańskie, bez europejskiego czy polskiego odpowiednika. Wybrane technologie (zwłaszcza te umożliwiające prewencję pierwotną i wtórną) mogą być z łatwością zastosowane przez szerokie grupy pacjentów i pracowników ochrony zdrowia, potrzeba jednak szeroko zakrojonych kampanii informacyjnych, edukacji i rekomendacji w tym zakresie.
Summary
The aim of this publication is to illustrate the realistic potential of digital technologies – mobile applications, telemedicine, automated analysis systems applied in the several key elements in stroke patient management. According to data provided by WHO, it is estimated that strokes have caused 5.78 million deaths in 2016.
Review has been conducted searching for digital health technologies used for stroke management in PubMed database, and several references have been snowballed from the search terms.
More and more scientific evidence speak for the efficiency of using digital technologies in care of stroke patients. Unfortunately, most of the recommendations linked to digitalization of patient care are part of guidelines provided by American associations, with no European or Polish equivalents. The chosen technologies (and especially those making primary and secondary prevention feasible) can be easily applied by wide groups of patients and healthcare practitioners. However, more publicly targeted informational and educational campaigns are necessary, together with the development of specific recommendations.
INTRODUCTION
The World Health Organization (WHO) defines stroke as the “interruption of the blood supply to the brain, usually because a blood vessel bursts or is blocked by a clot. This cuts off the supply of oxygen and nutrients, causing damage to the brain tissue” (1).
According to data provided by WHO, it is estimated that strokes have caused 5.78 million deaths in 2016, being the world’s second biggest killer (2).
Nowadays the majority of the strokes occurs in the younger age, unlike 30 years ago when they affected mostly people over 75 (3). The INTERSTROKE case-control study led in 32 nations around the world provided evidence that 10 risk factors represented 90% of the population-attributable risk for all stroke (4).
Guidelines written by The European Stroke Organisation (ESO) (5) distinguish a few key components to enhance stroke care:
1. Public Awareness and Education.
2. Primary Prevention.
3 Secondary Prevention.
4. Referral and Patient Transfer.
5. Emergency Management.
6. Stroke Services and Stroke Units.
7. Diagnostics.
8. General Stroke Treatment.
9. Specific Treatment.
10. Prevention and Management of Complications.
11. Rehabilitation.
In this paper the authors explore the most prominent developments in stroke care with a special focus on recent progress in the use of new and digital technologies. Multiple new definitions have been introduced to the public health domain. For instance, mHealth (mobile health) can be defined as a practice of medicine and public health services combined with the use of mobile devices (6). This term, however, is being replaced with broader term of “Digital Health” covering healthcare interventions delivered via digital technologies – telemedicine, Web-based strategies, e-mail, mobile phones, mobile applications, text messaging, and monitoring sensors (7). After a two-year process to update and standardize the typology, in December 2017 WHO released a revised classification scheme for digital health interventions, which “aims to promote an accessible and bridging language for health program planners to articulate functionalities of digital health implementations” (tab. 1) (8).
Tab. 1. Selected elements of stroke care according to ESO and the corresponding WHO digital health typology including examples
Element of stroke careDigital functionaity for addresing the health system challenge
Public awareness and education1.6.1. Client look-up of health information – mobile applications
2.8.1. Provide training content and reference material to healthcare provider(s) – mobile applications
Primary prevention4.1.4. Automated analysis of data to generate new information or predictions on future events – mobile applications
1.4.2. Self monitoring of health or diagnostic data by client – mobile applications
Secondary prevention1.4.3. Active data capture/documentation by client – mobile applications, wearable devices
Referral and patient transfer2.3.2. Provide checklist according to protocol – mobile applications
2.4.1. Consultations between remote client and healthcare provider – telemedicine
2.6.1. Coordinate emergency response and transport – telemedicine
Emergency management2.4.4. Consultations for case management between healthcare providers – telemedicine
2.8.1. Provide training content and reference material to healthcare provider(s) – mobile applications
Diagnostics2.7.2. Schedule healthcare provider’s activities – workflow management systems
4.1.4. Automated analysis of data to generate new information or predictions on future events – artificial intelligence system
2.4.3. Transmission of medical data (e.g. images, notes, and videos) to healthcare provider – telemedicine
2.4.4. Consultations for case management between healthcare providers – telemedicine
Rehabilitation2.4.1. Consultations between remote client and healthcare provider – telemedicine
1.4.2. Self monitoring of health or diagnostic data by client – virtual reality
The aim of this publication is to illustrate the realistic potential of digital technologies – mobile applications, telemedicine, automated analysis systems applied in the several key elements in stroke patient management.
In addition, another at-desk review has been conducted searching for digital health technologies used for stroke management in PubMed database, and several references have been snowballed from the search terms.
REVIEW
Mobile applications
By 2019, the number of smartphone users is estimated to raise to 2.5 billion people. A little more than 36 percent of the total population is anticipated to possess and use a smartphone by 2018, up from around 10 percent in 2011 (9). Mobile applications can be used to raise awareness among patients and healthcare professionals, therefore reducing financial burden from numerous disorders.
Mobile applications – Public Awareness and Education
Several mobile stroke applications can increase stroke awareness and help to perform early detections on mild stroke symptoms.
Some of the existing mobile health awareness applications are designed to raise knowledge and awareness around stroke and its consequences, including FAST Test (10), The Mayo Clinic Acute Stroke Evaluation App (11), Stroke 119 (12).
Mobile applications – Emergency Management
The National Institute of Health Stroke Scale (NIHSS) is the most widely used scale for the evaluation of basic neurological function in acute ischemic stroke, both initially and during its evolution. The scale can be used as a guideline for the development of both self-check (13) assessment for detecting mild stroke symptoms and by trained members of Emergency Medical Service staff (14). Several apps can be used to assist EMS staff in the application of the NIHSS in clinical practice (15).
Mobile applications – Primary Prevention, Secondary Prevention
In hope of increasing stroke awareness and improving stroke and NCD prevention (on an individual level), a new app was recently created by The National Institute for Stroke and Applied Neurosciences (AUT University) called the Stroke Riskometer™. The app is using recent studies from the field of risk presentation/communication and international guidelines on stroke and cardiovascular disease prevention which makes it a potentially important tool in general stroke prevention. Its algorithm is based on the Framingham Stroke Risk Score (FSRS) prediction algorithm (16) and is additionally improved by including several major risk factors based on the INTERSTROKE study (4). Stroke Riskometer™ estimates the absolute risk of stroke within the next 5 and 10 years for people aged > 20 years (17).

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Piśmiennictwo
1. WHO: Disease burden and mortality estimates [Internet]. World Health Organization. 2018; http://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html (dostęp z dnia: 19.12.2018).
2. WHO: Stroke, Cerebrovascular accident [Internet]. World Health Organization. 2018; https://www.who.int/topics/cerebrovascular_accident/en/ (dostęp z dnia: 19.12.2018).
3. Feigin V, Forouzanfar M, Krishnamurthi R et al.: Global and regional burden of stroke during 1990-2010: findings from the Global Burden of Disease Study 2010. Lancet 2014; 383(9913): 245-255.
4. O’Donnell MJ, Chin SL, Rangarajan S et al.: Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. Lancet 2016; 388(10046): 761-775.
5. The European Stroke Organisation (ESO) Executive Committee and the ESO Writing Committee: Guidelines for Management of Ischaemic Stroke and Transient Ischaemic Attack 2008. Cerebrovasc Dis 2008; 25: 457-507.
6. Adibi S (ed.): Mobile health: A technology road map. Springer, February 19, 2015: 1.
7. Widmer RJ, Collins NM, Collins CS et al.: Digital health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis. Mayo Clin Proc 2015; 90(4): 469-480.
8. World Health Organization (WHO): Classification of Digital Health Interventions v1.0: A Shared Language to Describe the Uses of Digital Technology for Health. Geneva: WHO; 2018; http://www.who.int/reproductivehealth/publications/mhealth/classification-digital-health-interventions/en/.
9. Statista: Number of smartphone users worldwide 2014-2020 [Internet]. Statista. 2018; https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/ (dostęp z dnia: 19.12.2018).
10. Home – Chest Heart & Stroke Scotland [Internet]. Chest Heart & Stroke Scotland. 2018; https://www.chss.org.uk (dostęp z dnia: 19.12.2018).
11. Rubin MN, Fugate JE, Barrett KM et al.: An acute stroke evaluation app: a practice improvement project. Neurohospitalist 2015; 5(2): 63-69.
12. Nam HS, Heo J, Kim J et al.: Development of smartphone application that aids stroke screening and identifying nearby acute stroke care hospitals. Yonsei Med J 2013; 55(1): 25-29.
13. Foong O, Yong J, Sulaiman S et al.: Mobile health awareness in pre-detection of mild stroke symptoms. Journal of Computer Science 2014; 10(12): 2383-2394.
14. Goldstein LB, Samsa GP: Reliability of the National Institutes of Health Stroke Scale: extension to non-neurologists in the context of a clinical trial. Stroke 1997; 28: 307-310.
15. Rodríguez-Prunotto L, Cano-de-la-Cuerda R: Aplicaciones móviles en el ictus: revisión sistemática. Rev Neurol 2018; 66(7): 213-229.
16. Cooney MT, Dudina A, D’Agostino R, Graham IM: Cardiovascular risk-estimation systems in primary prevention: do they differ? Do they make a difference? Can we see the future? Circulation 2010; 122: 300-310.
17. Parmar P, Krishnamurthi R, Ikram MA et al.: The Stroke RiskometerTM App: validation of a data collection tool and stroke risk predictor. Int J Stroke 2014; 10(2): 231-244.
18. Wolf PA, D’Agostino RB, Belanger AJ et al.: Probability of stroke: a risk profile from the Framingham study. Stroke 1991; 22: 312-318.
19. Masys DR: Telemedicine: A Guide to Assessing Telecommunications in Health Care. J Am Med Inform Assoc 1997; 4(2): 136-137.
20. Grigsby J, Sanders JH: Telemedicine: where it is and where it’s going. Ann Intern Med 1998; 129: 123-127.
21. Ganapathy K: Telemedicine and neurosciences. J Clin Neurosci 2005; 12: 851-862.
22. Levine SR, Gorman M: “Telestroke”: the application of telemedicine for stroke. Stroke 1999; 30: 464-469.
23. Waite K, Silver F, Jaigobin C et al.: Telestroke: a multi-site, emergency-based telemedicine service in Ontario. J Telemed Telecare 2006; 12(3): 141-145.
24. Gonzalez MA, Hanna N, Rodrigo ME et al.: Reliability of prehospital real-time cellular video phone in assessing the simplified National Institutes Of Health Stroke Scale in patients with acute stroke: a novel telemedicine technology. Stroke 2011; 42(6): 1522-1527.
25. Wechsler LR, Tsao JW, Levine SR et al.: Teleneurology applications: Report of the Telemedicine Work Group of the American Academy of Neurology. Neurology 2013; 80(7): 670-676.
26. Schwamm L, Audebert H, Amarenco P et al.: Recommendations for the Implementation of Telemedicine Within Stroke Systems of Care. Stroke 2009; 40(7): 2635-2660.
27. Demaerschalk BM, Bobrow BJ, Raman R et al.: Stroke Team Remote Evaluation Using a Digital Observation Camera (STRokE DOC) in Arizona – The Initial Mayo Clinic Experience (AZ TIME) Investigators. CT interpretation in a telestroke network: agreement among a spoke radiologist, hub vascular neurologist, and hub neuroradiologist. Stroke 2012; 43: 3095-3097.
28. Mitchell JR, Sharma P, Modi J et al.: A smartphone client-server teleradiology system for primary diagnosis of acute stroke. J Med Internet Res 2011; 13: e31.
29. Tchero H, Tabue Teguo M, Lannuzel A et al.: Telerehabilitation for Stroke Survivors: Systematic Review and Meta-Analysis. J Med Internet Res 2018; 20(10): e10867.
30. Samuel AL: Some studies in machine learning using the game of checkers. IBM J Res Dev 1959; 3(3): 210-229.
31. Wernick MN, Yang Y, Brankov JG et al.: Machine learning in medical imaging. IEEE Signal Process Mag 2010; 27: 25-38.
32. Krizhevsky A, Sutskever I, Hinton GE: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 2012; 25(2): 1097-1105.
33. Kamal H, Lopez V, Sheth SA: Machine Learning in Acute Ischemic Stroke Neuroimaging. Front Neurol 2018; 9: 945.
34. Aminov A, Rogers JM, Middleton S et al.: What do randomized controlled trials say about virtual rehabilitation in stroke? A systematic literature review and meta-analysis of upper-limb and cognitive outcomes. J Neuroeng Rehabil 2018; 15(1): 29.
35. Laver KE, Lange B, George S et al.: Virtual reality for stroke rehabilitation. Cochrane Database of Systematic Reviews 2017.
otrzymano: 2018-11-12
zaakceptowano do druku: 2018-12-03

Adres do korespondencji:
*Natasza Blek
Institute of Neuroscience and Cybernetic Medicine Faculty of Medicine Lazarski University, Warsaw
43 Swieradowska Str., 02-662 Warsaw, Poland
Phone: +48 (22) 5435330
E-mail: natasza.blek@lazarski.pl

Postępy Nauk Medycznych 6/2018
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