Exploring the impact of compassion and leadership on patient safety and quality in healthcare systems: a narrative review.

BACKGROUND: Patient safety and healthcare quality are considered integral parts of the healthcare system that are driven by a dynamic combination of human and non-human factors. This review article provides an insight into the two major human factors that impact patient safety and quality including compassion and leadership. It also discusses how compassion is different from empathy and explores the impact of both compassion and leadership on patient safety and healthcare quality. In addition, this review also provides strategies for the improvement of patient safety and healthcare quality through compassion and effective leadership. METHODS: This narrative review explores the existing literature on compassion and leadership and their combined impact on patient safety and healthcare quality. The literature for this purpose was gathered from published research articles, reports, recommendations and guidelines. RESULTS: The findings from the literature suggest that both compassion and transformational leadership can create a positive culture where healthcare professionals (HCPs) prioritise patient safety and quality. Leaders who exhibit compassion are more likely to inspire their teams to deliver patient-centred care and focus on error prevention. CONCLUSION: Compassion can become an antidote for the burnout of HCPs. Compassion is a behaviour that is not only inherited but can also be learnt. Both compassionate care and transformational leadership improve organisational culture, patient experience, patient engagement, outcomes and overall healthcare excellence. We propose that transformational leadership that reinforces compassion remarkably improves patient safety, patient engagement and quality.

Blockchain and COVID-19 pandemic: applications and challenges.

The year 2020 has witnessed the emergence of coronavirus (COVID-19) that has rapidly spread and adversely affected the global economy, health, and human lives. The COVID-19 pandemic has exposed the limitations of existing healthcare systems regarding their inadequacy to timely and efficiently handle public health emergencies. A large portion of today's healthcare systems are centralized and fall short in providing necessary information security and privacy, data immutability, transparency, and traceability features to detect fraud related to COVID-19 vaccination certification, and anti-body testing. Blockchain technology can assist in combating the COVID-19 pandemic by ensuring safe and reliable medical supplies, accurate identification of virus hot spots, and establishing data provenance to verify the genuineness of personal protective equipment. This paper discusses the potential blockchain applications for the COVID-19 pandemic. It presents the high-level design of three blockchain-based systems to enable governments and medical professionals to efficiently handle health emergencies caused by COVID-19. It discusses the important ongoing blockchain-based research projects, use cases, and case studies to demonstrate the adoption of blockchain technology for COVID-19. Finally, it identifies and discusses future research challenges, along with their key causes and guidelines.

An overview of radiation-induced heart disease.

Radiation therapy (RT) has dramatically improved cancer survival, leading to several inevitable complications. Unintentional irradiation of the heart can lead to radiation-induced heart disease (RIHD), including cardiomyopathy, pericarditis, coronary artery disease, valvular heart disease, and conduction system abnormalities. Furthermore, the development of RIHD is aggravated with the addition of chemotherapy. The screening, diagnosis, and follow-up for RIHD in patients who have undergone RT are described by the consensus guidelines from the European Association of Cardiovascular Imaging (EACVI) and the American Society of Echocardiography (ASE). There is compelling evidence that chest RT can increase the risk of heart disease. Although the prevalence and severity of RIHD are likely to be reduced with modern RT techniques, the incidence of RIHD is expected to rise in cancer survivors who have been treated with old RT regimens. However, there remains a gap between guidelines and clinical practice. Currently, therapeutic modalities followed in the treatment of RIHD are similar to the non-irradiated population. Preventive measures mainly reduce the radiation dose and radiation volume of the heart. There is no concrete evidence to endorse the preventive role of statins, angiotensin-converting enzyme inhibitors, and antioxidants. This review summarizes the current evidence of RIHD subtypes and risk factors and suggests screening regimens, diagnosis, treatment, and preventive approaches.

Precision Medicine in Patients with Differential Diabetic Phenotypes: Novel Opportunities from Network Medicine.

INTRODUCTION: Diabetes mellitus (DM) comprises differential clinical phenotypes ranging from rare monogenic to common polygenic forms, such as type 1 (T1DM), type 2 (T2DM), and gestational diabetes, which are associated with cardiovascular complications. Also, the high- -risk prediabetic state is rising worldwide, suggesting the urgent need for early personalized strategies to prevent and treat a hyperglycemic state. OBJECTIVE: We aim to discuss the advantages and challenges of Network Medicine approaches in clarifying disease-specific molecular pathways, which may open novel ways for repurposing approved drugs to reach diabetes precision medicine and personalized therapy. CONCLUSION: The interactome or protein-protein interactions (PPIs) is a useful tool to identify subtle molecular differences between precise diabetic phenotypes and predict putative novel drugs. Despite being previously unappreciated as T2DM determinants, the growth factor receptor-bound protein 14 (GRB14), calmodulin 2 (CALM2), and protein kinase C-alpha (PRKCA) might have a relevant role in disease pathogenesis. Besides, in silico platforms have suggested that diflunisal, nabumetone, niflumic acid, and valdecoxib may be suitable for the treatment of T1DM; phenoxybenzamine and idazoxan for the treatment of T2DM by improving insulin secretion; and hydroxychloroquine reduce the risk of coronary heart disease (CHD) by counteracting inflammation. Network medicine has the potential to improve precision medicine in diabetes care and enhance personalized therapy. However, only randomized clinical trials will confirm the clinical utility of network- oriented biomarkers and drugs in the management of DM.

Blockchain-Based Forward Supply Chain and Waste Management for COVID-19 Medical Equipment and Supplies.

The year 2020 has witnessed unprecedented levels of demand for COVID-19 medical equipment and supplies. However, most of today's systems, methods, and technologies leveraged for handling the forward supply chain of COVID-19 medical equipment and the waste that results from them after usage are inefficient. They fall short in providing traceability, reliability, operational transparency, security, and trust features. Also, they are centralized that can cause a single point of failure problem. In this paper, we propose a decentralized blockchain-based solution to automate forward supply chain processes for the COVID-19 medical equipment and enable information exchange among all the stakeholders involved in their waste management in a manner that is fully secure, transparent, traceable, and trustworthy. We integrate the Ethereum blockchain with decentralized storage of interplanetary file systems (IPFS) to securely fetch, store, and share the data related to the forward supply chain of COVID-19 medical equipment and their waste management. We develop algorithms to define interaction rules regarding COVID-19 waste handling and penalties to be imposed on the stakeholders in case of violations. We present system design along with its full implementation details. We evaluate the performance of the proposed solution using cost analysis to show its affordability. We present the security analysis to verify the reliability of the smart contracts, and discuss our solution from the generalization and applicability point of view. Furthermore, we outline the limitations of our solution in form of open challenges that can act as future research directions. We make our smart contracts code publicly available on GitHub.

COVID-19 Contact Tracing Using Blockchain.

Contact tracing has widely been adopted to control the spread of Coronavirus-2019 (COVID-19). It enables to identify, assess, and manage people who have been exposed to COVID-19, thereby preventing from its further transmission. Today's most of the contact tracing approaches, tools, and solutions fall short in providing decentralized, transparent, traceable, immutable, auditable, secure, and trustworthy features. In this paper, we propose a decentralized blockchain-based COVID-19 contact tracing solution. Contact tracing can greatly suffice the need for a speedy response to a pandemic. We leverage the immutable and tamper-proof features of blockchain to enforce trust, accountability, and transparency. Trusted and registered oracles are used to bridge the gap between on-chain and off-chain data. With no third parties involved or centralized servers, the users' medical information is not prone to invasion, hacking, or abuse. Each user is registered using their digital medical passports. To respect the privacy of the users, their locations are updated with a time delay of 20 minutes. Using Ethereum smart contracts, transactions are executed on-chain with emitted events and immutable logs. We present details of the implemented algorithms and their testing analysis. We evaluate the proposed approach using security, cost, and privacy parameters to show its effectiveness. The smart contracts code is publicly made available on GitHub.

Communication in Health Care: Impact of Language and Accent on Health Care Safety, Quality, and Patient Experience.

Permanent or temporary migration results in communication issues related to language barriers. The migrant's mother tongue is often different from that of the host country. Even when the same language is spoken, communication barriers arise because of differences in accent. These communication barriers have a significant negative impact on migrants accessing health care and their ability to understand instructions and seek follow-up care. A multidisciplinary team often has professionals from various countries. These migrant health care professionals find it difficult to communicate with patients of the host country and with their colleagues. Communication barriers, therefore, result in miscommunication or no communication between health care professionals and between health care professionals and patients. This increases the risk of medical errors and impacts quality of care and patient safety. This review looks at the impact of communication barriers in health care and endeavors to find effective solutions.

Blockchain for drug traceability: Architectures and open challenges.

Pharmaceutical supply chain (PSC) consists of multiple stakeholders including raw material suppliers, manufacturers, distributors, regulatory authorities, pharmacies, hospitals, and patients. The complexity of product and transaction flows in PSC requires an effective traceability system to determine the current and all previous product ownerships. In addition, digitizing track and trace process provides significant benefit for regulatory oversight and ensures product safety. Blockchain-based drug traceability offers a potential solution to create a distributed shared data platform for an immutable, trustworthy, accountable and transparent system in the PSC. In this paper, we present an overview of product traceability issues in the PSC and envisage how blockchain technology can provide effective provenance, track and trace solution to mitigate counterfeit medications. We propose two potential blockchain based decentralized architectures, Hyperledger Fabric and Besu to meet critical requirements for drug traceability such as privacy, trust, transparency, security, authorization and authentication, and scalability. We propose, discuss, and compare two potential blockchain architectures for drug traceability. We identify and discuss several open research challenges related to the application of blockchain technology for drug traceability. The proposed blockchain architectures provide a valuable roadmap for Health Informatics researchers to build and deploy an end-to-end solution for the pharmaceutical industry.

The role of blockchain technology in telehealth and telemedicine.

OBJECTIVE: Telehealth and telemedicine systems aim to deliver remote healthcare services to mitigate the spread of COVID-9. Also, they can help to manage scarce healthcare resources to control the massive burden of COVID-19 patients in hospitals. However, a large portion of today's telehealth and telemedicine systems are centralized and fall short of providing necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients' insurance claims and physician credentials. METHODS: The current study has explored the potential opportunities and adaptability challenges for blockchain technology in telehealth and telemedicine sector. It has explored the key role that blockchain technology can play to provide necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients' insurance claims and physician credentials. RESULTS: Blockchain technology can improve telehealth and telemedicine services by offering remote healthcare services in a manner that is decentralized, tamper-proof, transparent, traceable, reliable, trustful, and secure. It enables health professionals to accurately identify frauds related to physician educational credentials and medical testing kits commonly used for home-based diagnosis. CONCLUSIONS: Wide deployment of blockchain in telehealth and telemedicine technology is still in its infancy. Several challenges and research problems need to be resolved to enable the widespread adoption of blockchain technology in telehealth and telemedicine systems.

Improving Transfer Medication Reconciliation in an Emirati Tertiary Hospital Utilizing the Irish Health Service Executive Model.

Transfer is a vulnerable setting that increases the risk of medication errors. Medication reconciliation (MedRec) ensures accurate medication transfer at interfaces of care. It is addressed as a key performance indicator (KPI) in a tertiary hospital. The issue was failure to meet the KPI of more than 75%; the objective was to improve compliance with transfer MedRec. A quality improvement project was conducted utilizing physician active education, leadership support, and the Irish Health Service Executive (HSE) change model. Compliance with the KPI did not improve with monthly monitoring and physician education. Following leadership support, compliance increased from 56% to 72% but was not sustained. Adoption of the change model yielded a sustainable improvement from 65% to 81% within 1 year of the intervention and a reduction in medication errors. Improvement in the MedRec process requires a culture of accountability to change. HSE expedited stakeholders' engagement and implementation of the planned interventions.

Cardiovascular Care of the Oncology Patient During COVID-19: An Expert Consensus Document From the ACC Cardio-Oncology and Imaging Councils.

In response to the coronavirus disease 2019 (COVID-19) pandemic, the Cardio-Oncology and Imaging Councils of the American College of Cardiology offers recommendations to clinicians regarding the cardiovascular care of cardio-oncology patients in this expert consensus statement. Cardio-oncology patients-individuals with an active or prior cancer history and with or at risk of cardiovascular disease-are a rapidly growing population who are at increased risk of infection, and experiencing severe and/or lethal complications by COVID-19. Recommendations for optimizing screening and monitoring visits to detect cardiac dysfunction are discussed. In addition, judicious use of multimodality imaging and biomarkers are proposed to identify myocardial, valvular, vascular, and pericardial involvement in cancer patients. The difficulties of diagnosing the etiology of cardiovascular complications in patients with cancer and COVID-19 are outlined, along with weighing the advantages against risks of exposure, with the modification of existing cardiovascular treatments and cardiotoxicity surveillance in patients with cancer during the COVID-19 pandemic.

Blockchain for COVID-19: Review, Opportunities, and a Trusted Tracking System.

The sudden development of the COVID-19 pandemic has exposed the limitations in modern healthcare systems to handle public health emergencies. It is evident that adopting innovative technologies such as blockchain can help in effective planning operations and resource deployments. Blockchain technology can play an important role in the healthcare sector, such as improved clinical trial data management by reducing delays in regulatory approvals, and streamline the communication between diverse stakeholders of the supply chain, etc. Moreover, the spread of misinformation has intensely increased during the outbreak, and existing platforms lack the ability to validate the authenticity of data, leading to public panic and irrational behavior. Thus, developing a blockchain-based tracking system is important to ensure that the information received by the public and government agencies is reliable and trustworthy. In this paper, we review various blockchain applications and opportunities in combating the COVID-19 pandemic and develop a tracking system for the COVID-19 data collected from various external sources. We propose, implement, and evaluate a blockchain-based system using Ethereum smart contracts and oracles to track reported data related to the number of new cases, deaths, and recovered cases obtained from trusted sources. We present detailed algorithms that capture the interactions between stakeholders in the network. We present security analysis and the cost incurred by the stakeholders, and we highlight the challenges and future directions of our work. Our work demonstrates that the proposed solution is economically feasible and ensures data integrity, security, transparency, data traceability among stakeholders.

Ensuring protocol compliance and data transparency in clinical trials using Blockchain smart contracts.

BACKGROUND: Clinical Trials (CTs) help in testing and validating the safety and efficacy of newly discovered drugs on specific patient population cohorts. However, these trials usually experience many challenges, such as extensive time frames, high financial cost, regulatory and administrative barriers, and insufficient workforce. In addition, CTs face several data management challenges pertaining to protocol compliance, patient enrollment, transparency, traceability, data integrity, and selective reporting. Blockchain can potentially address such challenges because of its intrinsic features and properties. Although existing literature broadly discusses the applicability of blockchain-based solutions for CTs, only a few studies present their working proof-of-concept. METHODS: We propose a blockchain-based framework for CT data management, using Ethereum smart contracts, which employs IPFS as the file storage system to automate processes and information exchange among CT stakeholders. CT documents stored in the IPFS are difficult to tamper with as they are given unique cryptographic hashes. We present algorithms that capture various stages of CT data management. We develop the Ethereum smart contract using Remix IDE that is validated under different scenarios. RESULTS: The proposed framework results are advantageous to all stakeholders ensuring transparency, data integrity, and protocol compliance. Although the proposed solution is tested on the Ethereum blockchain platform, it can be deployed in private blockchain networks using their native smart contract technologies. We make our smart contract code publicly available on Github. CONCLUSIONS: We conclude that the proposed framework can be highly effective in ensuring that the trial abides by the protocol and the functions are executed only by the stakeholders who are given permission. It also assures data integrity and promotes transparency and traceability of information among stakeholders.

Evaluating the Impact of Patient No-Shows on Service Quality.

PURPOSE: Patient no-shows are long-standing issues affecting resource utilization and posing risks to the quality of healthcare services. They also lead to loss of anticipated revenue, particularly in services where resources are expensive and in great demand. METHODS: In order to address common reasons why patients miss appointments, this study reviews the current literature and investigates various tools and methods that have been implemented to mitigate such issues. Further, a case study is conducted to identify the rate of no-shows and underlying causes at a radiology department in one of the leading hospitals in the MENA region. RESULTS: Our results show that the no-shows are high due to multiple factors, such as patient behavior, patients' financial situation, environmental factors and scheduling policy. CONCLUSION: In conclusion, we generate a list of recommendations that can help in reducing the rate of patient no-shows, such as patient education, application of dynamic scheduling policies and effective appointment reminder systems to patients.

Artificial Intelligence: The Future for Diabetes Care.

Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition. Principles of machine learning have been used to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications. Digital therapeutics have proven to be an established intervention for lifestyle therapy in the management of diabetes. Patients are increasingly being empowered for self-management of diabetes, and both patients and health care professionals are benefitting from clinical decision support. AI allows a continuous and burden-free remote monitoring of the patient's symptoms and biomarkers. Further, social media and online communities enhance patient engagement in diabetes care. Technical advances have helped to optimize resource use in diabetes. Together, these intelligent technical reforms have produced better glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin. AI will introduce a paradigm shift in diabetes care from conventional management strategies to building targeted data-driven precision care.

Evaluation of System Modelling Techniques for Waste Identification in Lean Healthcare Applications.

PURPOSE: Waste identification plays a vital role in lean healthcare applications. While the value stream map (VSM) is among the most commonly used tools for waste identification, it may be limited to visualize the behaviour of dynamic and complex healthcare systems. To address this limitation, system modelling techniques (SMTs) can be used to provide a comprehensive picture of various system-wide wastes. However, there is a lack of evidence in the current literature about the potential contribution of SMTs for waste identification in healthcare processes. METHODS: This study evaluates the usability and utility of six types of SMTs along with the VSM. For the evaluation, interview-based questionnaires were conducted with twelve stakeholders from the outpatient clinic at the Heart and Vascular Institute at Cleveland Clinic Abu Dhabi. RESULTS: VSM was found to be the most useful diagram in waste identification in general. However, some SMTs that represent the system behaviour outperformed the VSM in identifying particular waste types, e.g., communication diagram in identifying over-processing waste and flow diagram in identifying transportation waste. CONCLUSION: As behavioural SMTs and VSM have unique strengths in identifying particular waste types, the use of multiple diagrams is recommended for a comprehensive waste identification in lean. However, limited resources and time, as well as limited experience of stakeholders with SMTs, may still present obstacles for their potential contribution in lean healthcare applications.

Blockchain-Based Solution for COVID-19 Digital Medical Passports and Immunity Certificates.

COVID-19 has emerged as a highly contagious disease which has caused a devastating impact across the world with a very large number of infections and deaths. Timely and accurate testing is paramount to an effective response to this pandemic as it helps identify infections and therefore mitigate (isolate/cure) them. In this paper, we investigate this challenge and contribute by presenting a blockchain-based solution that incorporates self-sovereign identity, re-encryption proxies, and decentralized storage, such as the interplanetary file systems (IPFS). Our solution implements digital medical passports (DMP) and immunity certificates for COVID-19 test-takers. We present smart contracts based on the Ethereum blockchain written and tested successfully to maintain a digital medical identity for test-takers that help in a prompt trusted response directly by the relevant medical authorities. We reduce the response time of the medical facilities, alleviate the spread of false information by using immutable trusted blockchain, and curb the spread of the disease through DMP. We present a detailed description of the system design, development, and evaluation (cost and security analysis) for the proposed solution. Since our code leverages the use of the on-chain events, the cost of our design is almost negligible. We have made our smart contract codes publicly available on Github.

Application of Artificial Intelligence in the Health Care Safety Context: Opportunities and Challenges.

There is a growing awareness that artificial intelligence (AI) has been used in the analysis of complicated and big data to provide outputs without human input in various health care contexts, such as bioinformatics, genomics, and image analysis. Although this technology can provide opportunities in diagnosis and treatment processes, there still may be challenges and pitfalls related to various safety concerns. To shed light on such opportunities and challenges, this article reviews AI in health care along with its implication for safety. To provide safer technology through AI, this study shows that safe design, safety reserves, safe fail, and procedural safeguards are key strategies, whereas cost, risk, and uncertainty should be identified for all potential technical systems. It is also suggested that clear guidance and protocols should be identified and shared with all stakeholders to develop and adopt safer AI applications in the health care context.

Impact of repeated hospital accreditation surveys on quality and reliability, an 8-year interrupted time series analysis.

OBJECTIVE: To evaluate whether hospital re-accreditation improves quality, patient safety and reliability over three accreditation cycles by testing the accreditation life cycle model on quality measures. DESIGN: The validity of the life cycle model was tested by calibrating interrupted time series (ITS) regression equations for 27 quality measures. The change in the variation of quality over the three accreditation cycles was evaluated using the Levene's test. SETTING: A 650-bed tertiary academic hospital in Abu Dhabi, UAE. PARTICIPANTS: Each month (over 96 months), a simple random sample of 10% of patient records was selected and audited resulting in a total of 388 800 observations from 14 500 records. INTERVENTIONS: The impact of hospital accreditation on the 27 quality measures was observed for 96 months, 1-year preaccreditation (2007) and 3 years postaccreditation for each of the three accreditation cycles (2008, 2011 and 2014). MAIN OUTCOME MEASURES: The life cycle model was evaluated by aggregating the data for 27 quality measures to produce a composite score (YC) and to fit an ITS regression equation to the unweighted monthly mean of the series. RESULTS: The results provide some evidence for the validity of the four phases of the life cycle namely, the initiation phase, the presurvey phase, the postaccreditation slump and the stagnation phase. Furthermore, the life cycle model explains 87% of the variation in quality compliance measures (R2=0.87). The best-fit ITS model contains two significant variables (ß1 and ß3) (p≤0.001). The Levene's test (p≤0.05) demonstrated a significant reduction in variation of the quality measures (YC) with subsequent accreditation cycles. CONCLUSION: The study demonstrates that accreditation has the capacity to sustain improvements over the accreditation cycle. The significant reduction in the variation of the quality measures (YC) with subsequent accreditation cycles indicates that accreditation supports the goal of high reliability.