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A Comprehensive Financial and Organizational Analysis of Telerehabilitation Business Models for Sustainable Growth and Market Expansion in the Healthcare Sector Dhirenbhai Kalal
The Eastasouth Management and Business Vol. 2 No. 03 (2024): The Eastasouth Management and Business (ESMB)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esmb.v2i03.1143

This shift marked the end of the pandemic era, where telerehabilitation was merely an emergency measure, and the beginning of a new growth trajectory for digital health, with the global market estimated at USD 5.32 billion in 2024 and projected to grow to USD 11.81 billion by 2030 at a CAGR of 13.2 per cent. However, the financial and organisational basis of this growth is poorly documented, to varying degrees, in the different clinical indications. This paper presents a financial and organisational evaluation of fourteen peer-reviewed and industry-based sources to create a holistic view of business models for telerehabilitation. Evidence is presented under three themes: Business model frameworks in digital health, Health-economic evidence for telerehabilitation, and Clinical and organisational implementation. Results found that per-patient savings ranged from USD 565.66 to USD 2,352.00, with four of eight studies showing moderate results for combined neurological and cardiological populations; per-patient savings were found to be favourable, with ninety-two per cent of reviewed cardiac studies showing favourable results for protocolized, exercise-based cardiac interventions; and results were least consistent for heterogeneous musculoskeletal indications. Financial sustainability of telerehabilitation businesses was identified through business model syntheses as having common elements such as diversified revenue streams, partnerships with payers and with technology vendors, and adaptive governance. Analysis shows that rather than a common telerehabilitation approach, business model design is the most defendable path to sustainable market expansion.

A Strategic Operations Management Framework for Enhancing Service Efficiency, Therapist Productivity, and Patient Flow Optimization in Outpatient Physical Therapy Clinics Dhirenbhai Kalal
The Eastasouth Management and Business Vol. 2 No. 02 (2024): The Eastasouth Management and Business (ESMB)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esmb.v2i02.1144

Outpatient Physical Therapy (OPT) clinics are facing increasing pressure to balance patient demand, strict therapist staffing levels, patient non-attendance and productivity demands dictated by reimbursement requirements. This paper brings together results from twenty peer-reviewed and thesis-level papers covering healthcare scheduling optimization, physical therapist productivity measurement, patient no-show prediction, and lean process redesign to present an integrated Strategic Operations Management Framework for outpatient physical therapy environments. It combines a predictive scheduling optimization system with a real-time capacity allocation system for therapists and a lean-based patient-flow monitoring loop, based on the evidence gained from a discrete-event simulation and mathematical programming. Synthesized results show that a simulation-optimization scheduling approach can increase therapist utilisation to around 91 per cent and decrease the incidence of no shows up to 27 per cent over rule-based block scheduling, and lean healthcare interventions can lead to a median decrease in patient waiting time by 31 per cent and process cycle time by 26 per cent. Productivity indices that have been created over the past 40 years from departmental evaluations reveal a cumulative increase of about 58 percent, while documented risks of clinician burnout have occurred when trying to increase productivity without workforce protections. This proposed framework is divided into four interacting zones: predictive scheduling, lean process redesign, dynamic capacity management and workforce well-being, and provides a staged implementation roadmap. Limitations associated with the retrospective and cross setting nature of the synthesized evidence are discussed, as are implications for clinic administrators, payers, and workforce policy.

Artificial Intelligence-Driven Clinical Decision Support Systems for Improving Diagnostic Accuracy and Personalized Treatment Planning in Physical Therapy Dhirenbhai Kalal
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 03 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i03.1146

As physical therapy practice moves toward the increasingly large and complex multimodal patient data stream (imaging, gait kinetics, wearable-sensor streams, and patient-reported outcomes), relying on unaided clinical judgment is insufficient for pattern recognition. Twenty-five papers were retrieved between 2018 and 2025 for artificial intelligence (AI) clinical decision support systems (CDSS) related to diagnostic accuracy and personalized treatment planning in physical therapy.Twenty-five papers were identified between 2018 and 2025 for AI clinical decision support systems (CDSS) for diagnostic accuracy and personalized treatment planning in physical therapy. The deep-learning models for knee osteoarthritis grading, low back pain classification, and sarcopenia-related gait screening are analyzed, as well as the large language model-based clinical reasoning models, multi-sensor rehabilitation-monitoring platforms, and myoelectric control systems for upper-limb recovery. The reported diagnostic accuracy of imaging-based models ranges from 86.2% to 92.5% and the evidence from the network meta-analysis suggests that the improvement of pain and ROM outcomes by AI-assisted rehabilitation is greater than conventional rehabilitation. The main barriers to the adoption are clinician trust, burden of integration to the workflow, and data-privacy concerns; while the facilitators are explainability and demonstrated diagnostic benefit. Ethical, legal, and regulatory issues related to the use of AI-CDSS in rehabilitation are also explored in the synthesis. The results confirm the hybrid model (clinician in the loop) of integrating AI-CDSS within the task of physical therapist judgment.

Development and Evaluation of an Internet of Things Based Wearable Sensor System for Real-Time Monitoring and Remote Management of Musculoskeletal Rehabilitation Patients Dhirenbhai Kalal
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i01.1148

Musculoskeletal rehabilitation needs people to keep track of how patients are moving after they leave the clinic. The usual way of following up with patients after they leave the clinic has some problems. Patients can only visit the clinic often they have to tell us how they are feeling and some patients live really far from the clinic. This paper looks at twenty-one studies that were published between 2019 and 2025. These studies are about using Internet of Things based sensor systems to monitor musculoskeletal rehabilitation patients in real time. The patients in these studies were recovering from things like knee arthroplasty, post-stroke upper-limb impairment and hip and knee osteoarthritis. The systems that were studied have three parts: sensing, edge or network and cloud or application layers. The paper examines how these systems operate, such as how they detect movement, how they communicate with one another and how they interpret data. The paper also examines the tracking capabilities of these systems – such as movement, muscle activity, patient feelings and how they communicate this information to doctors. The paper compares these systems and discovers that some systems are more adept at tracking leg motion, and others, arm motion. Systems with inertial measurement units, for instance, are able to follow leg motion, while systems with surface electromyography and accelerometers are able to follow arm motion in stroke patients. In one study, they looked at many studies and found that patients who used remote monitoring systems were less likely to require rehospitalization, and they were not in the hospital as long. They were also not required to come into the clinic frequently, and were more likely to adhere to their treatment plan. In a few small studies, which used an inertial platform to track the patients after knee replacement surgery, it was determined that these devices were effective and there was no loss of accuracy if utilized within the home environment. The ability to interact with other computers, and learn from the result of the data collected, is the key to making these systems effective, it concludes the paper. The biggest problems that are still preventing these systems from being widely used are that they do not work well with systems they are not secure and there are problems, with how they will be paid for through 2025. Although the rehabilitation of musculoskeletal and the Internet of Things based sensor systems are improving, they still have some challenges to be addressed. In the future, the Internet of Things (IoT) sensor systems will continue to be employed in musculoskeletal rehabilitation.

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