Innovating Healthcare through Remote Monitoring

2016 ◽  
Vol 7 (1) ◽  
pp. 49-66 ◽  
Author(s):  
Faustina Acheampong ◽  
Vivian Vimarlund
Keyword(s):  
Decision Making ◽  
Business Model ◽  
Remote Monitoring ◽  
Clinical Decision Making ◽  
Healthcare Delivery ◽  
Clinical Decision ◽  
Remote Monitoring System ◽  
Home Based ◽  
The Government ◽  
Remote Patient

Information technology has been suggested to improve patient health outcomes and reduce healthcare cost. This study explored the business model and effects of collaborative innovation between caregivers and patients on healthcare delivery through remote patient monitoring by interviewing caregivers and surveying atrial fibrillation patients. Findings indicate that remote monitoring enhanced early detection of potential risks and quality of clinical decision-making with patients feeling more empowered and involved in their own care. The remote monitoring system which consisted of a home-based ECG and a web-based service and was offered free to patients, brought together caregivers, patients, service provider and the government as actors. The introduction of remote monitoring increased the workload of caregivers and facilitation of timely diagnostics and decision-making were not realized. IT is an enabler of innovation in healthcare, but it must be integrated into work processes with a viable business model to realize potential benefits and sustain it.

Innovating Healthcare through Remote Monitoring

2017 ◽  
pp. 247-268
Author(s):  
Faustina Acheampong ◽  
Vivian Vimarlund
Keyword(s):  
Decision Making ◽  
Business Model ◽  
Remote Monitoring ◽  
Clinical Decision Making ◽  
Healthcare Delivery ◽  
Work Processes ◽  
Home Based ◽  
Patient Health ◽  
The Government ◽  
Remote Patient

Information technology has been suggested to improve patient health outcomes and reduce healthcare cost. This study explored the business model and effects of collaborative innovation between caregivers and patients on healthcare delivery through remote patient monitoring by interviewing caregivers and surveying atrial fibrillation patients. Findings indicate that remote monitoring enhanced early detection of potential risks and quality of clinical decision-making with patients feeling more empowered and involved in their own care. The remote monitoring system which consisted of a home-based ECG and a web-based service and was offered free to patients, brought together caregivers, patients, service provider and the government as actors. The introduction of remote monitoring increased the workload of caregivers and facilitation of timely diagnostics and decision-making were not realized. IT is an enabler of innovation in healthcare, but it must be integrated into work processes with a viable business model to realize potential benefits and sustain it.

Effects of a Home-Based Monitoring Device on Innovation in Healthcare Delivery

2013 ◽  
pp. 316-334
Author(s):  
Faustina Acheampong ◽  
Vivian Vimarlund
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Healthcare Delivery ◽  
Clinical Decision ◽  
Home Based ◽  
Collaborative Innovation ◽  
Patient Health ◽  
Potential Benefits ◽  
Improve Patient

Information technology has been suggested to improve patient health outcomes and reduce the burden of care. In this study, we explored the effects of collaborative innovation between caregivers and patients on healthcare delivery as a consequence of the use of an IT-based device by patients with atrial fibrillation. Two cardiologists and two nurses were interviewed while questionnaires were mailed to 75 patients querying them about the use of a home-based ECG for remote monitoring. Findings indicated that the caregivers considered the device to enhance the quality of clinical decision-making. Patients found the device to be useful and felt more involved in their own care. However, the introduction of the device presented work overload for the caregivers. Thus, the facilitation of timely diagnostics and decision-making were not realized. IT is an enabler through which innovation in healthcare delivery can be realized, but it must be integrated into work practices to realize potential benefits.

scholarly journals Rapid development of a Clinical Decision-Making Committee in a UK paediatric hospital during the COVID-19 pandemic

2020 ◽  
Vol 20 (2) ◽  
pp. 167-180 ◽  
Author(s):  
Rick Turnock ◽  
Will Weston ◽  
Nicki Murdock ◽  
Amel Alghrani ◽  
Conor Mallucci ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Ethical Decision Making ◽  
Rapid Development ◽  
Clinical Decision ◽  
Paediatric Hospital ◽  
Ethical Frameworks ◽  
National Guidance ◽  
The Government ◽  
Set Up

To date, the Government has not issued any national ethical guidance to support clinical decision-making in England during periods of potentially reduced healthcare resources in the context of the evolving COVID-19 1 pandemic at the time of writing. In the ensuing vacuum left by a lack of national guidance, ethical frameworks and approaches have been drafted by professional bodies, individual hospitals and trusts. It is clear that in delivering healthcare during this pandemic, more specific guidance is needed to ensure fair and consistent allocation policies, to attain public trust and confidence and to support clinicians so that decisions do not fall on them to make alone and unsupported. This article sets out how we in our institution, a UK tertiary and secondary level stand-alone paediatric provider Trust, set up a Clinical Decision-Making Committee to inform proactive clinical and ethical decision-making, to ensure that all patients are treated appropriately and fairly during these unprecedented times.

scholarly journals Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study (Preprint)

2021 ◽  
Author(s):  
Stina Matthiesen ◽  
Søren Zöga Diederichsen ◽  
Mikkel Klitzing Hartmann Hansen ◽  
Christina Villumsen ◽  
Mats Christian Højbjerg Lassen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Decision Support ◽  
Qualitative Study ◽  
Real World ◽  
Remote Monitoring ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Great Promise ◽  
Prediction Tool

BACKGROUND Artificial intelligence (AI), such as machine learning (ML), shows great promise for improving clinical decision-making in cardiac diseases by outperforming statistical-based models. However, few AI-based tools have been implemented in cardiology clinics because of the sociotechnical challenges during transitioning from algorithm development to real-world implementation. OBJECTIVE This study explored how an ML-based tool for predicting ventricular tachycardia and ventricular fibrillation (VT/VF) could support clinical decision-making in the remote monitoring of patients with an implantable cardioverter defibrillator (ICD). METHODS Seven experienced electrophysiologists participated in a near-live feasibility and qualitative study, which included walkthroughs of 5 blinded retrospective patient cases, use of the prediction tool, and questionnaires and interview questions. All sessions were video recorded, and sessions evaluating the prediction tool were transcribed verbatim. Data were analyzed through an inductive qualitative approach based on grounded theory. RESULTS The prediction tool was found to have potential for supporting decision-making in ICD remote monitoring by providing reassurance, increasing confidence, acting as a second opinion, reducing information search time, and enabling delegation of decisions to nurses and technicians. However, the prediction tool did not lead to changes in clinical action and was found less useful in cases where the quality of data was poor or when VT/VF predictions were found to be irrelevant for evaluating the patient. CONCLUSIONS When transitioning from AI development to testing its feasibility for clinical implementation, we need to consider the following: expectations must be aligned with the intended use of AI; trust in the prediction tool is likely to emerge from real-world use; and AI accuracy is relational and dependent on available information and local workflows. Addressing the sociotechnical gap between the development and implementation of clinical decision-support tools based on ML in cardiac care is essential for succeeding with adoption. It is suggested to include clinical end-users, clinical contexts, and workflows throughout the overall iterative approach to design, development, and implementation.

scholarly journals A review of features and characteristics of smart medication adherence products

2021 ◽  
pp. 171516352110341
Author(s):  
Sadaf Faisal ◽  
Jessica Ivo ◽  
Tejal Patel
Keyword(s):  
Decision Making ◽  
Medication Adherence ◽  
Remote Monitoring ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Smart Products ◽  
Unit Dose ◽  
Definition Of ◽  
The Cost ◽  
Patient Use

Background: Smart medication adherence products (smart MAPs) capture and transmit real-time medication intake by using various means of connectivity, allowing for remote monitoring. Numerous such products with different features are available to address medication nonadherence. A comparison of the features of these products is needed for clinical decision-making. Therefore, the objective of this review was to compare smart MAPs available for in-home use. Methods: We searched grey and published literature and videos to identify smart MAPs. To be considered smart, products required 2 features: connectivity (the ability for collected data to exist outside the physical device) and automaticity (the ability for data to be analyzed or processed automatically). Products were excluded if product descriptions were not available in English, not for in-home use and unable to dispense medications. Results: Of the 51 products identified, 38 commercially available and 13 prototypes met the definition. Of these, 75% ( n = 38) contained alarms, 24% ( n = 12) were unit-dose, 63% ( n = 32) were multidose, 43% ( n = 22) had locking features, 41% ( n = 21) were portable and 88% ( n = 45) sent notifications to patients. The cost of marketed products, excluding subscriptions, ranged from $10 to $1500 USD. Some products required a monthly ( n = 16) or yearly ( n = 1) subscription ranging from $10 to $100 USD. Discussion: There is a growing market of smart MAPs for in-home patient use with variable features. Clinicians can use these features to identify and recommend products according to the specific needs of their patients to address medication adherence. Can Pharm J (Ott) 2021;154:xx-xx.

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