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Repetitive transcranial magnetic stimulation (rTMS) is a well-established treatment with efficacy for several psychiatric disorders and has yielded promising yet mixed data showing reductions in craving for substance use. Patients with substance use disorders and comorbid depression may encounter obstacles to receiving rTMS in outpatient settings for treatment of depression. In turn, implementation of rTMS in residential substance use programs would greatly benefit those with comorbid treatment resistant depression. This paper aims to provide recommendations for implementing rTMS within residential substance use treatment centers.

Using PubMed, the authors conducted a narrative review of manuscripts using various combinations of the following search terms: rTMS, depression, substance use and substance use disorder. The authors read manuscripts for their methodology, outcomes and adverse events to synthesize their results, which correspond to their recommendations for patient selection, safely implementing rTMS in residential substance use facilities and optimal rTMS protocols to start with.

Advantages of this approach include increased compliance, monitoring and access to care. Recommendations to safely incorporate rTMS in residential substance use disorder treatment centers revolve around selection of patients eligible for rTMS, allowing for sufficient time to elapse prior to commencing rTMS, monitoring for signs of recent substance use or withdrawal and using rTMS protocols compatible with the therapeutic programming of a treatment center.

This paper details the challenges and benefits of implementing rTMS for patients with dual diagnosis and provides recommendations to safely do so. To the best of the authors’ knowledge, this is a novel and unpublished endeavor.

This study examines how service feedback and physician popularity affect physician demand in the context of virtual healthcare environment. Based on the signaling theory, the critical factor of environment uncertainty (i.e. disease risk) and its impact on physician demand is also investigated. Further, the research on the endogeneity of online reviews in healthcare is also examined in the current study.

A secondary data econometric analysis using 3-wave data sets of 823 physicians obtained from two PRWs (Healthgrades and Vitals) was conducted. The analysis was run using the difference-in-difference method to consider physician and website-specific effects.

The study's findings indicate that physician popularity has a stronger positive effect on physician demand compared with service feedback. Improving popularity leads to a relative increase in the number of appointments, which in turn enhance physician demand. Further, the impact of physician popularity on physician demand is positively mitigated by the disease risk.

The authors' research contributes to a better understanding of the signaling transmission mechanism in the online healthcare environment. Further, the findings provide practical implications for key stakeholders into how an efficient feedback and popularity mechanism can be built to enhance physician service outcomes in order to maximize the financial efficiency of physicians.

This quantitative phase of a mixed-methods study aims to describe the effect of the Transitional Care Bridge (TCB) programme on functional decline, mortality, health-care utilisation and health outcomes compared to usual care in a regional hospital in the Netherlands.

In a pre- and post-cohort study, patients aged ≥70 years, admitted to the hospital for ≥48 h and discharged home with an Identification of Seniors at Risk score of ≥2, were included. The TCB programme, started before discharge, encompassed six visits by the community nurse (CN). Data were obtained from the hospital registry and by three questionnaires over a three months period, addressing activities of daily living (ADL), self-rated health, self-rated quality of life and health-care utilisation.

In total, 100 patients were enrolled in this study, 50 patients in the TCB group and 50 patients in the usual care group. After three months, 36.7% was dependent on ADL in the TCB group compared to 47.1% in the usual care group. Mean number of visits by the CN in the TCB group was 3.8. Although the TCB group had a lower mortality, this study did not find any statistically significant differences in health outcomes and health-care utilisation.

Challenges in the delivery of the programme may have influenced patient outcomes. More research is needed on implementation of evidence-based programmes in smaller research settings. A qualitative phase of the study needs to address these outcomes and explore the perspectives of health professionals and patients on the delivery of the programme.

This study provides valuable information on the transitional care programme in a smaller setting.

The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.

This data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.

The proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.

The research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.

The emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.

In this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

Understanding the outcomes of co-creation (CC) in healthcare is increasingly gaining multidisciplinary scientific interest. Although more and more service management scholars have pointed out the benefits of cross-fertilization between the various research fields, the literature on this topic is still scattered and poorly integrated. This study aims to summarize and integrate multiple strands of extant knowledge CC by identifying the outcomes of health CC and the determinants of these outcomes and their relationships.

A structured literature review was conducted per PRISMA guidelines. A total of 4,189 records were retrieved from the six databases; 1,983 articles were screened, with 161 included in the qualitative thematic analysis.

This study advances a comprehensive framework for healthcare CC based on a thorough analysis of the outcomes and their determinants, that is, antecedents, management activities and institutional context. Extant research rarely evaluates outcomes from a multidimensional and systemic perspective. Less attention has been paid to the relationship among the CC process elements.

This study offers an agenda to guide future studies on healthcare CC. Highlighting some areas of integration among different disciplines further advances service literature.

The framework offers an operational guide to better shape managerial endeavors to facilitate CC, provide direction and assess multiple outcomes.

This is the first extensive attempt to synthesize and integrate multidisciplinary knowledge on CC outcomes in healthcare settings by adopting a systematic perspective on the overall process.

Leveraging information technology (IT) to improve the treatment and support of patients is a widely studied topic in healthcare. For chronic diseases, such as diabetes, the use of information technology is even more important since its effect extends from a clinic environment to patients’ daily life. The purpose of this paper is to investigate the impacts of one widely adopted information technology, the mobile phone, on diabetes treatment, specifically on the complicated process of patients’ health, emotions and compliance.

We leverage a unique longitudinal dataset on diabetes patients’ health status in rural areas of China to study the problem. We also cross-link the dataset with mobile carrier data to further differentiate mobile phone use to phone calls and network use. To address the endogeneity concerns, we apply PSM and a series of instrument variables.

We identify clear evidence that mobile phone use can significantly improve patients’ emotions and compliance, where the effect is generally larger on patients in worse health conditions. While mobile phone calls clearly benefit diabetes patients, we do notice that mobile phone network use has a negative moderating effect with patients’ health condition on improving compliance.

This study not only enriches our theoretical understanding of the role of mobile phones in diabetes management, it also shows the economic benefit of promoting patients’ use of mobile phones, which should be considered by medical care providers and medical policymakers.

The current study is aimed at identifying the prominent influencers that affect the response behaviour of patients in a hospital environment.

The research is based on the data collected through the participant observation method while interviewing patients about the quality of healthcare services in nine community health centres of the Kashmir division. Thematic analysis was performed on the information collected from patients admitted to various hospital sections.

The analysis of the qualitative data revealed that the presence of hospital staff near respondents, perceived risk of maltreatment, social desirability, the sensitivity of the topic, risk of information sharing and attitude towards surveys are the most frequently observed factors that modulate the patient's tendency to truthfully report critical facts about the problem understudy.

These results can help researchers to exercise caution while communicating with respondents and collecting data related to serious issues in a natural setting.

The purpose of this research paper is to explore the determinants of patients' service choices between telephone consultation and text consultation in online health communities (OHCs).

This study utilized an empirical model based on the elaboration likelihood model and examined the effect of information, regarding service quality (the central route) and service price (the peripheral route), using online health consultation data from one of the largest OHCs in China.

The logistic regression results indicated that both physician- and patient-generated information can influence the patients' service choices; service price signals will lead patients to cheaper options. However, individual motivations, disease risk and consulting experience change a patients' information processing regarding central and peripheral cues.

Previous researchers have investigated the mechanism of patient behavior in OHCs; however, the researchers have not focused on the patients' choices regarding the multiple health services provided in OHCs. The findings of this study have theoretical and practical implications for future researchers, OHC designers and physicians.

The purpose of the study is to measure the performance of Indian hospitals, both operationally and financially, by using hospital KPI's. The assessment is predominantly done by linking it to the existing Lean practices in Indian hospitals.

An empirical study based on cross-sectional survey of hospital managers and specialists in various private healthcare facilities across India was conducted to validate the proposed Lean framework. From an extensive literature survey, the authors identified quality, delivery, efficiency, accessibility and patient centeredness to be the main operational performance (OP) indicators for hospitals. Business or financial performance was measured based on parameters which are average revenue per occupied bed (ARPOB), earnings before interest, tax, depreciation and amortization (EBITDA) and operating revenue. Confirmatory Factor Analysis (CFA) was carried out using a specialized technique, called Structural Equation Modelling(SEM) and an explicit factor structure was hypothesized.

Management commitment towards Lean in hospitals is statistically proven to have impacted operational and financial performance. However, leanness in technology and business processes showed no statistical significance on either operational or financial performance parameters. Hospital stakeholders showed statistical significance on though it had no impact on the financial performance. Results obtained from the statistical analysis indicate a positive impact of hospital Lean practices on timely delivery of services and improved service quality. Efficiency, accessibility of services and patient centered behavior in hospital operations could not be statistically proven to have impacted the financial performance.

Effectiveness of Lean management (LM) principles in improving hospital operations is largely dependent on patient centered behavior. Empowered employees who are trained to add value from a customer view point, make hospital operations safe and improved. Properly trained and communicated employees who are committed to quality improvements can make a positive impact on patients' quality of life and thus positively impact the society. The study lists ways to attain the required outcomes.

This paper is among the very few that has attempted to suggest ways to link implementation of Lean practices more effectively in Indian hospitals to improve hospital performance at operational and financial levels.

Recent research suggests that more than two-thirds of people use online reviews to find a new primary care physician (PCP). However, it is unclear what role review content plays when a patient uses online reviews to decide about a new PCP. This paper aims to understand how a review's content, related to competence (communication and technical skills) and benevolence (fidelity and fairness), impacts patients’ trusting intentions to select a PCP. The authors build the model around information diagnosticity, construal level theory and valence asymmetries and use review helpfulness as a mediator and review valence as a moderator in this process.

The authors use two experimental studies to test their hypotheses and collect data through prolific.

The authors find that people have a harder time making inferences about the technical and communication skills of a PCP. Reviews about fidelity are perceived as more helpful and influential in building trust than reviews about fairness. Overall, reviews about the communication skills of a PCP have stronger effects on trusting intentions than other types of reviews. The authors also find that positive reviews are perceived as more helpful for the readers than negative reviews, but negative reviews have a stronger impact on patients' trust intentions than positive ones.

The authors identify how online reviews about a PCP’s competency and benevolence affect patients’ trusting intentions to choose the PCP. The implication of findings of this study for primary medical practice and physician review websites is discussed.