Willingness to pay for medical treatments in chronic diseases: a multicountry survey of patients and physicians

The healthcare industry has historically been an early adopter of technology advancements and has reaped significant benefits. Machine learning (an artificial intelligence subset) is being used in a variety of health-related fields, including the invention of new medical treatments, the management of patient data and records, and the treatment of chronic diseases. One of the most important uses of machine learning in healthcare is the detection and diagnosis of diseases and conditions that are otherwise difficult to identify. This can range from tumors that are difficult to detect in their early stages to other hereditary illnesses. This research identifies and discusses the various usages of machine learning in medical diagnosis.

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Title: The Willingness To Pay for Telemedicine By Patients With Chronic Diseases: A Systematic Review (Preprint)

10.2196/preprints.33372 ◽

2021 ◽

Author(s):

Valerie Chua ◽

Jin Hean Koh ◽

Gerald Koh ◽

Shilpa Tyagi

Keyword(s):

Systematic Review ◽

Willingness To Pay ◽

Chronic Diseases ◽

Disease Incidence ◽

Remote Access ◽

Future Research ◽

Inclusion Criteria ◽

Factors Associated ◽

Effectiveness Analysis ◽

Planning Processes

BACKGROUND Telemedicine is increasingly being leveraged on, with the need for remote access to healthcare driven by the rise of chronic disease incidence and the COVID-19 pandemic. It is also important to understand patients’ willingness to pay (WTP) for telemedicine and the factors contributing towards it as this knowledge may inform health policy planning processes like resource allocation or developing a pricing strategy for telemedicine services. Most of the published literature is focused on cost-effectiveness analysis findings to guide financing from health system’s perspective. However, there is limited exploration of the WTP from a patient’s perspective which is pertinent for sustainability of an intervention. OBJECTIVE To address this gap, we aimed to conduct a systematic review to describe the WTP for telemedicine interventions and to identify the factors influencing WTP among patients with chronic diseases in developed settings. METHODS We systematically searched 4 databases (PubMed, PsycInfo, Embase, EconLit). Two authors were involved in the appraisal. Studies were included if they reported the WTP amounts and/or identified the factors associated with patients’ WTP, included patients above 18 years of age, those diagnosed with chronic diseases and were from developed settings. RESULTS 11 studies from 7 countries met the study’s inclusion criteria. The proportion of people willing to pay for telemedicine ranged from 19% to 70% across the studies, while the values for WTP amounts ranged from 0.885 USD to 821.25 USD. We found statistically significant correlation of age and distance to preferred health facility with WTP for telemedicine: while higher age was associated with lower WTP, longer travel distance was associated with higher WTP. CONCLUSIONS Based on our findings, the following are recommendations which may enhance WTP: exposure to the telemedicine intervention prior to assessing WTP, lowering of telemedicine costs, and providing patient education to raise awareness on telemedicine’s benefits and address patients’ concerns. Additionally, we recommend future research be directed at standardizing the reporting of WTP studies with adoption of a common metric for WTP amount ,which may facilitate generalization of findings and effect estimates.

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Willingness to pay for pharmacist-provided home telemonitoring among patients with chronic diseases in Enugu metropolis

Health Informatics Journal ◽

10.1177/1460458219852534 ◽

2019 ◽

Vol 26 (2) ◽

pp. 829-840

Author(s):

Chibueze Anosike ◽

Maxwell Ogochukwu Adibe ◽

Abdulmuminu Isah ◽

Onyinye Blessing Ukoha-Kalu

Keyword(s):

Confidence Interval ◽

Willingness To Pay ◽

Chronic Diseases ◽

Odds Ratio ◽

Contingent Valuation Method ◽

Cross Sectional Study ◽

Cross Sectional ◽

Health Insurance Status ◽

Valuation Method ◽

Home Telemonitoring

Home telemonitoring is a promising approach in the management of patients with chronic diseases. However, no study has assessed its acceptability and possible service charge in Nigeria. Therefore, this study aimed to evaluate willingness to pay for pharmacist-provided telemonitoring among patients with chronic diseases and to explore its determinants. Hence, using the contingent valuation method, a cross-sectional study was conducted among eligible patients visiting 15 selected community pharmacies in Enugu metropolis, over a period of 3 months. Of the 335 patients who participated in the study, about 40 percent (i.e. 39.4%) were willing to pay an average monthly fee of ₦915.91 ± 485.49 (US$2.99 ± 1.59) for home telemonitoring services. Significant predictors of willingness to pay for home telemonitoring were perceived insufficient income (odds ratio = 0.20, 95% confidence interval = 0.07–0.60, p = 0.040) and health insurance status (odds ratio = 0.39, 95% confidence interval = 0.18–0.86, p = 0.019). Our findings suggest a promising potential for adopting home telemonitoring services among patients with chronic diseases in Enugu metropolis.

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Adoption of machine learning for medical diagnosis

10.31219/osf.io/t69w5 ◽

2021 ◽

Author(s):

Mohammed Yousef Shaheen

Keyword(s):

Machine Learning ◽

Chronic Diseases ◽

Medical Diagnosis ◽

Patient Data ◽

Healthcare Industry ◽

Early Adopter ◽

Medical Treatments ◽

Health Related ◽

Detection And Diagnosis ◽

Diagnosis Of Diseases

The healthcare industry has historically been an early adopter of technologyadvancements and has reaped significant benefits. Machine learning (an artificialintelligence subset) is being used in a variety of health-related fields, including theinvention of new medical treatments, the management of patient data and records, andthe treatment of chronic diseases. One of the most important uses of machine learningin healthcare is the detection and diagnosis of diseases and conditions that areotherwise difficult to identify. This can range from tumors that are difficult to detect intheir early stages to other hereditary illnesses. This research identifies and discussesthe various usages of machine learning in medical diagnosis.

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Workplace Support Systems in Small- and Medium-Sized Companies for Employees Receiving Medical Treatment in Japan

Global Journal of Health Science ◽

10.5539/gjhs.v12n3p91 ◽

2020 ◽

Vol 12 (3) ◽

pp. 91

Author(s):

Ryoko Rikitake ◽

Satoru Kamitani ◽

Miyako Takahashi ◽

Takahiro Higashi

Keyword(s):

Medical Treatment ◽

Chronic Diseases ◽

Support Systems ◽

Medical Condition ◽

Marketing Research ◽

Medical Treatments ◽

Work Arrangements ◽

Medical Needs ◽

Workplace Policies ◽

Therapeutic Needs

BACKGROUND: Maintaining one’s current job is important for patients. Few studies have investigated the presence of support systems in small- and medium-sized companies to help balance the therapeutic needs and occupational roles of workers in Japan. AIMS: To understand whether small- and medium-sized companies in Japan have established workplace policies to help employees with chronic disease balance medical treatment and professional life. METHODS: We surveyed a sample of small- and medium-sized companies in Japan identified from a large database of corporate credit and marketing research companies between February and March 2017. A questionnaire addressed workplace policies that supported employees’ medical treatments and professional lives, such as flexible work arrangements and the preparation of manuals and forms to facilitate communication with treating physicians. RESULTS: Of the 4158 companies initially contacted, 1140 companies (27%) responded to the survey. Of the valid respondents, 21% of the workplaces reported having established sufficient office rules to address employee’s necessary medical needs. Approximately half of the workplaces (53%) shared that they had a system in place to provide temporary medical leave for employees with chronic diseases. Few (12%) workplaces had established a process for having a trial return to work after a period of absence due to a medical condition. CONCLUSIONS: Currently, a minority of small- and medium-sized companies in Japan have established workplace policies to address the medical needs of employees with chronic diseases.

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Relationship Between Lifestyle in Relation to Food Quality, Food Quantity and Exercise Versus Estimated Risk Probabilities of Having 5 Metabolic Disorder Diseases Using 8 Pathologies of Metabolic Syndrome’s Intra-Cellular Pathways and Collected Data Over 6.5 Years Based on GH-Method: Math-Physical Medicine (No. 497)

Journal of Clinical & Experimental Immunology ◽

10.33140/jcei.06.06.01 ◽

2021 ◽

Vol 6 (6) ◽

Keyword(s):

Chronic Diseases ◽

Human Body ◽

Physical Object ◽

Processed Foods ◽

Post Injury ◽

Food Category ◽

Medical Treatments ◽

Food Quantity ◽

Original Strength ◽

External Forces

The author was a professionally trained mathematician, physicist and engineer. His view of health and medicine is similar to his past experience on designing a physical object such as a building structure or a working machine, which he calls an “object”. The object’s strength or its expected lifespan is similar to the health conditions and longevity of a human being based on the following three key factors: (1) The original strength of the object’s material which is similar to the genetic factors of a human body. Generally speaking, the genetic (not “epigenetic”) portion only contributes 15% to 30%, approximately 20% or less, of having various chronic diseases. (2) The quality of engineering design and construction or manufacturing of this object are similar to the fundamental influential causes, including lifestyle details, life-long bad habits, and environmental damaging factors on human health. Among the external causes, one category that has the most impact on health is food, particularly with processed foods causing the most damage. Therefore, he tries to exclude all kinds of processed foods from his own food category. (3) The object suffers from different operational problems due to external forces or impacts which are similar to various diseases affecting humans. After the object suffers from external forces or impacts such as an earthquake or hurricane, we must use some structural reinforcements to fix the problems or replace the malfunctioned parts of the machine. These engineering after-actions are similar to the medical “treatments” post-injury/infection provided to patients by doctors. The medical treatments include medication interventions (either oral drugs or biochemical injections), necessary surgeries or organ transplants, which are similar to the engineering repair of the damaged object. Nevertheless, all type of medical treatments bring some degree of traumatic effects on the human body. In addition, up to now, there are no medications which can cure chronic diseases induced by metabolic disorders. The different medications given to patients only suppress the symptoms of different chronic diseases and do not deal with the root causes. Therefore, they are not able to reverse, repair, or cure chronic diseases.

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Subjective Time Preference and Willingness to Pay for an Energy-Saving Durable Good

Zeitschrift für Sozialpsychologie ◽

10.1024//0044-3514.32.3.133 ◽

2001 ◽

Vol 32 (3) ◽

pp. 133-141 ◽

Cited By ~ 4

Author(s):

Gerrit Antonides ◽

Sophia R. Wunderink

Keyword(s):

Willingness To Pay ◽

Energy Saving ◽

Subjective Time ◽

Durable Good ◽

Discount Function ◽

Hyperbolic Discount ◽

The One ◽

Two Parameter ◽

Difference Effect ◽

Better Than

Summary: Different shapes of individual subjective discount functions were compared using real measures of willingness to accept future monetary outcomes in an experiment. The two-parameter hyperbolic discount function described the data better than three alternative one-parameter discount functions. However, the hyperbolic discount functions did not explain the common difference effect better than the classical discount function. Discount functions were also estimated from survey data of Dutch households who reported their willingness to postpone positive and negative amounts. Future positive amounts were discounted more than future negative amounts and smaller amounts were discounted more than larger amounts. Furthermore, younger people discounted more than older people. Finally, discount functions were used in explaining consumers' willingness to pay for an energy-saving durable good. In this case, the two-parameter discount model could not be estimated and the one-parameter models did not differ significantly in explaining the data.

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