APJCP 18(10): 2775
4. Synthesis and extraction of research thematic
The transformation of digital healthcare for sustainability has been an attractive discipline of knowledge discovery. Fig. 2 presents a word cloud of the reviewed studies and the holistic mind map, which depicts current research trends for sustainable digital healthcare. To present a tabular view of reviewed studies, a coding table was also developed (Table A1 in appendix) during the review process. This section covers an interpretation and evaluation of research focus and the purposes and limitations found in the studied papers. Moreover, it includes the contribution of technology vendors and their supported field trials, cited in different studies but not included in the review due to non-compliance with the set inclusion criteria of review. This section also includes a brief description of the identified trends and research gaps.
Table 4 (continued) Study
Reference Highlights
[107] •Introduced a Big Data readmission risk analysis framework based on HPCC (High Performance Computing Cluster).
•It adopts the Naive Bayes classification algorithm.
•The framework demonstrates that overall assessment time can be significantly decreased using a parallel computing platform and Big Data analytics, while maintaining performance.
[108] •Introduced BD2Decide project, a BD based Integrated DSS that provides necessary information for tailored treatment and care pathways for neck and head cancer patients.
•The system was validated in a clinical study with 1000 patients.
[109] •Proposed a BD based predictive analytics model for Asthma prediction
•Applies random forest method to develop a classifier model for predicting asthma disease condition in future.
•Uses Apache Spark framework to perform BD predictive analysis.
This helps to reduce computational complexity caused by parallelism.
[110] •Proposed a predictive application for Total Knee Replacement (TKR) surgery based on BD and fuzzy logic.
•Analyses patients’ attributes (age, gender, health condition, economic status, medical records history) to predict the length of stay at hospital after surgery.
•Uses fuzzy logic rules to trace and track patients’ attributes and recommends the needed preparation.
[111] •Proposed a BD based platform Übiquitous Health Profile (ÜHPr).
•Offers foundational support to archive heterogeneous health data and achieve interoperability (partially) in the healthcare domain.
[112] •Proposed a rehabilitation system for PD patients based on BD, IoT, and AI.
•Elaborates the role of robots in Parkinson’s Disease treatment using BD.
•Uses laser scanned scheme with dynamic piece-wise linear Gaussian time warp (ML).
[113] •Introduced a novel healthcare monitoring framework in cloud environment based on BD.
•Classifies patients’ health condition by using their medical data (diabetes, blood pressure, mental health, drug review data).
•Proposed BD engine based on ontologies, data mining techniques, and bidirectional long short-term memory (Bi- LSTM).
•Proposed ontologies deliver semantic knowledge about entities, aspects, and their relations with diabetes and blood pressure domain.
[114] •Proposed a BD analytics system based on FPGA (Field Programmable Gate Array) processor.
•Includes three types of processes (ML based executions) 1st : pre-processing (to eliminate noise from image or avoid irrele- vant data).
2nd : applies feature selection based decision tree technique 3rd : applies stage-based ML classification technique (for accuracy analysis).
International Journal of Medical Informatics 149 (2021) 104420
8 4.1. Thematic outcome
Research of VR and AR technologies in the reviewed research found a focus on elder care and rehabilitation; for providing assistance, moni- toring (visualization) and analysis. The identified limitations include vendor specific hardware [22–25,28–31,36], user interface and usability [23,31,36], limited features [24,25,29,31
–33,35–37,115], validation[26,27,31,32,34,38], accuracy [30,36,37] and cost [24,25]. As DCHC is a practice-oriented means of verifying, the practicality of research contributions is to benchmark the findings with recent solutions from technology as given in Table 5.
AI, ML and DL studies selected for this review focused on elder care and different chronic diseases. The observed purposes included assis- tance, monitoring, self-care and self-management, diagnosis, risk pre- diction, well-being awareness, and personalized healthcare. The identified limitations include user interface and usability [52,58,67,77], limited features [52,55,58,60,62,65,66,71,72,76] and resource [59,72], interoperability [54,58,67,69,70], scalability [61,65,67,68,72,74,76,
77], scalability [74,127], vendor specific hardware [60], and validation[54
–58,62–64,68]. Beside the reviewed studies, a few notable contri-butions from technology vendors and their collaborators were also observed in AI for digital healthcare (Table 6).
Compared to other emerging information technologies in this
review, DT is a very new technology in the healthcare industry. The reviewed studies of DT focused on elder care and chronic disease. Their purpose included monitoring, diagnosis, detection, and prediction.
Limitations interpreted include compatibility [78,81,86] (in existing settings), adaptability [78,81,86], security [82,85], cost and validation [78,81–84,86].
BD research studies focused on solutions for healthcare providers and services. The identified purposes include decision support, assis- tance, assessment, risk prediction, qualitative and/or quantitative ser- vice quality enhancement. The identified limitations included scalability [103,105,106,110
–113], interoperability [98,99,109–111], compati-bility (in existing settings) [70,99,100,102,104,107,108,114], limited features [101,112,113], cost (interpreted) and validation [98,102,104,
107–109,114].Data centricity in healthcare is essential. It is fundamental to facili- tating time-optimised healthcare planning and service delivery. The concept of data centricity was initially perceived and actualized in the early 90 s, through the introduction of EHRs. In the beginning the application of data centricity was limited to providers only. Patient participation was passive (information access only). With time and technological advancements, the need for data centricity has increased and changed. Data centricity now demands the active participation of all stakeholders (including patients) in all decision-making aspects of healthcare. Responding to this timely demand is only possible with the effective implementation and utilization of emerging information tech- nologies into digital health. Data-centric healthcare has the potential to reduce medication error, contain health costs and improve decision making.
Sustainability of large-scale data-centric digital health deployments is vital. Such deployments involve considerable technological resource, infrastructure, and financial investment. To achieve sustainability in digital health it is important to ensure user-centric design, data privacy and protection measures, transparency, interoperability, scalability, and
Fig. 2. Word cloud of research literature.3 www.immersivetouch.com
4 www.orcam.com
5 www.microsoft.com/en-us/hololens
6 www.google.com/glass
7 www.philips.com/a-w/research/research-programs/augmented-reali ty-surgical-navigation.html
8 Kno.e.sis
9 www.deepgenomics.com
10 https://github.com/google/deepvariant
A. Zahid et al.
Table 5
Benchmarking AR solutions for healthcare from technology vendors with research publications.
Solution Name Description Contributor(s) Reference ImmersiveTouch3 AR platform offering
different surgical training modules for aneurysm clipping, ventriculostomy, percutaneous spinal screw placement and other various techniques. In the architecture, the user holds a high- resolution stereoscopic display and a haptic stylus on top of the display, which provides a very realistic surgical simulation.
ImmersiveTouch® and University of Illinois (US)
[116]
OrCam4 Portable device to provide artificial vision by mounting a mini television camera on the spectacles frame for optical character recognition using a microchip. The device is capable of detecting patterns (including face) and texts. It uses an algorithm to convert the detected item(s) into words (can be heard using earpiece). It is an example of an AI based wearable device for patients with vision disability.
OrCam [117,
118]
Cave AR setup called to
facilitate drug addiction rehabilitation. The setup uses goggles which turn images projected on the walls of a room (cave) into a 3DHD (Three Dimensional High Definition) experience.
University of
Houston [119]
HoloLens5 AR headset, able to project a realistic virtual experience in the surrounding environment. The visor can be calibrated depending on users and can illustrate high quality 3D images in the visual surface. 3D speakers execute binaural audio at any point on the virtual surface. The speciality of HoloLens is that it does not disrupt the user’s view and hearing of the real world surroundings.
Microsoft Inc. [120]
Virtual Endovascular Trainer
Delivers training for various modules (including aneurysm
3D Systems Inc. [121,122, 123]
Table 5 (continued)
Solution Name Description Contributor(s) Reference and acute stroke) with
haptic feedback to enhance surgical efficiency and decrease patient exposure to fluoroscopy in the course of procedures.
Google Glass6 A combination of minicomputer, prism screen and projector in a pair of spectacles.
It is able to project any image (detected on neuro-monitoring or navigation) and directly visualise the detection to the surgeon’s glasses. The product eliminates the need to look away from the surgical context.
Google Inc. [120,124, 125]
Augmented Reality Surgical Navigation technology7
AR based solution combining 3D X-ray images with optical imaging. The solution is able to provide a unique AR view of a patient’s spine, plan the best execution path, and consequently place pedicle screws though AR navigation (automatic) during surgical procedures.
This innovation eliminates the use of the CT scanner and reduces medicine doses.
Phillips [17]
kHealth–ADHF8 Augmented personalized healthcare (APH)- based framework to ensure improved health fitness, decision making and wellbeing. The app (kHealth–ADHF) seamlessly monitors a patient through targeted questions derived from application specific knowledge (cardiovascular). The app also uses sensors to retrieve patient data: heart rate, blood pressure and weight.
This data is transferred to the mobile app via Bluetooth. The app analyses the answers and sensor data and generates alerts based on the assessments.
They have since introduced another app called
“kHealth–asthma” for Asthma children to ensure better control
Ohio Centre of Excellence in Knowledge-enabled Computing (US)
[126]
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International Journal of Medical Informatics 149 (2021) 104420
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compatibility within the solution. Consideration of these characteristics in solution design, development, and evaluation (validation) is therefore significant. As
Table A1(see appendix) shows, evaluation of several solutions is still in progress. It is expected that the contributors will consider the discussed needs in their evaluation process to deliver a sustainable solution for a prospective predictive universal digital healthcare ecosystem.
4.2. Identification of trends and gaps