scholarly journals Evaluating Digital Maturity and Patient Acceptability of Real-Time Patient Experience Feedback Systems: Systematic Review

2019 ◽  
Vol 21 (1) ◽  
pp. e9076 ◽  
Author(s):  
Mustafa Khanbhai ◽  
Kelsey Flott ◽  
Ara Darzi ◽  
Erik Mayer
Keyword(s):  
Systematic Review ◽  
Real Time ◽  
Patient Experience ◽  
Feedback Systems ◽  
Patient Acceptability

scholarly journals Evaluating Digital Maturity and Patient Acceptability of Real-Time Patient Experience Feedback Systems: Systematic Review (Preprint)

2017 ◽  
Author(s):  
Mustafa Khanbhai ◽  
Kelsey Flott ◽  
Ara Darzi ◽  
Erik Mayer
Keyword(s):  
Real Time ◽  
Patient Experience ◽  
Digital Technology ◽  
Health Care Services ◽  
Health Management ◽  
Essential Elements ◽  
Digital Systems ◽  
Cochrane Library ◽  
Feedback Systems ◽  
Patient Acceptability

BACKGROUND One of the essential elements of a strategic approach to improving patients’ experience is to measure and report on patients’ experiences in real time. Real-time feedback (RTF) is increasingly being collected using digital technology; however, there are several factors that may influence the success of the digital system. OBJECTIVE The aim of this review was to evaluate the digital maturity and patient acceptability of real-time patient experience feedback systems. METHODS We systematically searched the following databases to identify papers that used digital systems to collect RTF: The Cochrane Library, Global Health, Health Management Information Consortium, Medical Literature Analysis and Retrieval System Online, EMBASE, PsycINFO, Web of Science, and CINAHL. In addition, Google Scholar and gray literature were utilized. Studies were assessed on their digital maturity using a Digital Maturity Framework on the basis of the following 4 domains: capacity/resource, usage, interoperability, and impact. A total score of 4 indicated the highest level of digital maturity. RESULTS RTF was collected primarily using touchscreens, tablets, and Web-based platforms. Implementation of digital systems showed acceptable response rates and generally positive views from patients and staff. Patient demographics according to RTF responses varied. An overrepresentation existed in females with a white predominance and in patients aged ≥65 years. Of 13 eligible studies, none had digital systems that were deemed to be of the highest level of maturity. Three studies received a score of 3, 2, and 1, respectively. Four studies scored 0 points. While 7 studies demonstrated capacity/resource, 8 demonstrated impact. None of the studies demonstrated interoperability in their digital systems. CONCLUSIONS Patients and staff alike are willing to engage in RTF delivered using digital technology, thereby disrupting previous paper-based feedback. However, a lack of emphasis on digital maturity may lead to ineffective RTF, thwarting improvement efforts. Therefore, given the potential benefits of RTF, health care services should ensure that their digital systems deliver across the digital maturity continuum.

Measuring Patient Experience In Real Time Using IBeacon Technology

2019 ◽  
Vol 12 (2) ◽  
pp. 230-238
Author(s):  
Dalal Al-Alqusair ◽  
Isra Al-Turaiki ◽  
Abeer Al-Humaimeedy ◽  
Ghada Alhudhud
Keyword(s):  
Real Time ◽  
Patient Experience

scholarly journals Real-time patient experience surveys of hospitalized medical patients

2016 ◽  
Vol 11 (4) ◽  
pp. 251-256 ◽  
Author(s):  
Kimberly Indovina ◽  
Angela Keniston ◽  
Mark Reid ◽  
Katherine Sachs ◽  
Chi Zheng ◽  
...  
Keyword(s):  
Real Time ◽  
Patient Experience ◽  
Medical Patients

scholarly journals Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2727
Author(s):  
Hari Prasanth ◽  
Miroslav Caban ◽  
Urs Keller ◽  
Grégoire Courtine ◽  
Auke Ijspeert ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gait Analysis ◽  
Real Time ◽  
Wearable Sensors ◽  
Pressure Sensors ◽  
Clinical Applications ◽  
Performance Comparison ◽  
Heel Strike ◽  
Rule Based ◽  
Pathological Gait

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

scholarly journals Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review

2021 ◽  
Vol 28 (1) ◽  
pp. e100262
Author(s):  
Mustafa Khanbhai ◽  
Patrick Anyadi ◽  
Joshua Symons ◽  
Kelsey Flott ◽  
Ara Darzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Patient Experience ◽  
Language Processing ◽  
Performance Metrics ◽  
Free Text ◽  
Patient Feedback

ObjectivesUnstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.MethodsDatabases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.ResultsNineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.ConclusionNLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.

scholarly journals Systematic Review on Human Skin-Compatible Wearable Photoplethysmography Sensors

2021 ◽  
Vol 11 (5) ◽  
pp. 2313
Author(s):  
Inho Lee ◽  
Nakkyun Park ◽  
Hanbee Lee ◽  
Chuljin Hwang ◽  
Joo Hee Kim ◽  
...  
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Real Time ◽  
Human Skin ◽  
State Of The Art ◽  
Electronic Devices ◽  
Semiconducting Materials ◽  
Physiological Conditions ◽  
Technological Developments ◽  
Monitoring Technologies

The rapid advances in human-friendly and wearable photoplethysmography (PPG) sensors have facilitated the continuous and real-time monitoring of physiological conditions, enabling self-health care without being restricted by location. In this paper, we focus on state-of-the-art skin-compatible PPG sensors and strategies to obtain accurate and stable sensing of biological signals adhered to human skin along with light-absorbing semiconducting materials that are classified as silicone, inorganic, and organic absorbers. The challenges of skin-compatible PPG-based monitoring technologies and their further improvements are also discussed. We expect that such technological developments will accelerate accurate diagnostic evaluation with the aid of the biomedical electronic devices.

Systematic review showed measures of individual burden of osteoarthritis poorly capture the patient experience

2013 ◽  
Vol 66 (8) ◽  
pp. 826-837 ◽  
Author(s):  
Lucy Busija ◽  
Richard H. Osborne ◽  
Carol Roberts ◽  
Rachelle Buchbinder
Keyword(s):  
Systematic Review ◽  
Patient Experience
Sign in / Sign up

Export Citation Format

Share Document