Health Care Solutions
Health care solutions are a necessity in an evolving world where patient care and outcomes are prioritized. For years, health care solutions have been transforming the way health care is delivered to auxiliary practices and methods that seek to diagnose, treat, and mitigate the symptoms of chronic, severe diseases. Health care solutions can help augment health care systems facing systematic challenges and incentivize patient care and outcomes. These include, but are not limited to, the need: for health solutions to improve decision-making skills; to focus on patient care and treatment; to detect and diagnose at earlier rates; to provide more convenient treatment with quicker services and innovation; to produce an easier-to-swallow option in terms of cost and value; and to prioritize the improvement of care and activity, and complex signals that are related to the possibility of the advancement and development of the disease set. Quality and safety issues introduce or announce chronic or severe diseases that require advanced health care solutions to foresee or develop better handling. As knowledge of diseases and conditions advances, it takes a hearty team of medical innovators and discoverers to determine how to handle, diagnose, and reduce the effects of normal drugs, procedures, and medications available in the market. Reimagining health care is a necessity in the world of health care. The global health care system is not mandated or required to be compliant with integrating health care solutions mentioned before. While health care systems undergo change regularly and are always in alignment with the latest and greatest technologies, see the following methods to further advance the integration of new health care system technologies.
Defining Health Care Solutions
Health care solutions comprise new ways of working, systems, and technology that can be used to improve patient care and outcomes. These solutions allow for clinician focus on best practice patient management rather than on the day-to-day operation of the health system. Health care solutions therefore seek to disrupt key elements of how patients currently interact with or are managed within health care systems, as well as changing where, how, what, who, and the cost of health care service delivery, management, and operations. Health care solutions can thus be considered beyond care solutions, which describe systemic interventions to improve patient care and health system operations.
There are different ways that a healthcare solution can be designed, although usually this would incorporate either of the two main elements, or sometimes both; the solutions can be a technology-driven solution that focuses on technological advancements, or a care-driven solution that is founded on the patient. These types of health care solutions are certainly aligned with the global challenges and unmet needs described, and the best practice interventions. Because the unmet needs, current solutions, and therefore, the health care challenges can differ greatly across healthcare fields, the definition of “solution”/”healthcare solution” should be well specified during the writing and revision of specific diseases. It is vital that healthcare stakeholders are involved in defining the solutions. Overall, the potential types of healthcare solutions are patient-driven, care-driven, and process-driven, all of which have the ability to transform healthcare to deliver better outcomes for patients more effectively and efficiently.
Technological Advancements in Health Care
Technological advancements are revolutionizing the health care sector. Electronic health records now make it easier for practitioners to maintain a continuum of care history, while mobile health apps and devices help patients track vital signs and health statuses. Technology is reshaping how practitioners provide care and has a significant influence in shaping administrative processes as well as the potential to close access gaps for underserved patients. Data management systems can now help provide more detailed patient outcomes and insights into why patient populations might perform differently than others. Patients are also playing a bigger role in bridging their care between visits and more closely monitoring and addressing concerns prior to a physician’s visit. Telehealth programs across the world are reducing costs and further enhancing long-term patient engagement.
Software and digital tools that use robotics are moving from initial platforms providing electronic records to utilizing workflows and myriad other systems to improve efficiency. The global patient engagement solutions market is set to grow significantly due to technological advancements; improving interoperability and the advent of innovative care delivery. As healthcare technology continues to evolve, the benefits of a better patient experience, higher clinical efficiencies, drug management, and ensuring a better overall experience for all involved are significant benefits. In concert with the continued growth of electronic health records specifically into broader care coordination and accountable care initiatives, vendors are adding new capabilities to their solutions for language services, telehealth, and patient portals. More fully integrated telemedicine and telehealth systems that include key input and output points such as web cameras, electronic blood pressure cuffs, pulse oximeters, video chat, and secure messaging are the future of the specialty.
Telemedicine and Remote Monitoring
In 21st-century health care, technology has revolutionized patient care and improved numerous patient outcomes. In particular, the advent of telemedicine or telehealth has considerably increased access, speed, and efficacy of patient care. Telehealth modalities can include interactive video sessions, remote patient monitoring, home health services, secure messaging, and phone consults. While telehealth is very useful in rural areas with large geographical services, it is often used in urban and metropolitan areas to improve access and lifestyle convenience. In particular, remote patient monitoring uses in-home devices to continuously monitor patient vitals, blood glucose levels, cardiac rhythms, and other important health markers, providing physicians with the information they need to understand an individual’s health without requiring them to visit a medical facility. Further, such remote patient monitoring can save money by managing chronic disease and detecting warning signs before they become life-threatening. Remote patient monitoring can thereby reduce emergency room visits and allow for timely interventions to avoid inpatient hospitalizations. Therefore, the goal of telemedicine, and specifically remote patient monitoring, is to provide improved outcomes for the patient, decreased healthcare costs, and improved patient satisfaction with their care.
Telemedicine, and remote patient monitoring in particular, have been shown to be vital during a crisis, such as a situation where individuals were required to protect themselves from public infection. The rush towards virtual consults is expected to continue for a significant fraction of the population. However, major obstacles to remote patient monitoring adaptability remain, such as establishing telemedicine protocols, payer willingness to become consistent with practice, and implementation of remotely accessible technologies. Other challenges include the acceptance of remote patient monitoring technologies and the legal and regulatory changes necessary to promote interconnectedness, either from the hospital or healthcare system or the user end of the physician-patient relationship. However, there also exists a concern in developing a meta-model of health service—such as telehealth—so effective that we lose the proverbial ‘individual trees for the forest.’ In other words, with the recent incorporation of telehealth services into the more generalized health system, many services that could be virtualized are being centralized into the more generalized health service. As such, while telehealth is seen as a 21st-century version of healthcare, the goal in telehealth is to manage treatments virtually and reduce the consequences of travel on the patient. Thus, in a futures market, health systems additionally need a virtual travel division.
Data Analytics and Artificial Intelligence in Health Care
Health care is not an exception when it comes to making use of data analytics and artificial intelligence. Advanced technology has revolutionized the way treatment options are identified and care is delivered. Trust me that you need help more than a doctor’s decision to make sure a specific treatment is capable of improving the outcome of the one managed. The provision of appropriate care usually takes deep attention, considering the diversity of underlying factors that determine the best therapy plan for a patient. The solution to the evolving complexities of care is the machining of patient data, considering factors that determine the recovery rate and the set of actions influential in assisting in relieving the individual of stress.
Data analytics and artificial intelligence have revolutionized the decision-making process and treatment of patients, especially in disease diagnosis because of their pattern recognition capabilities and the personalization of responses to patients based on information retrieved from their personal experiences with similar patients. Furthermore, the use of predictive analytics has seen improvement in performance optimization and cost reduction in healthcare operational efficiency and patient re-admission. However, the industry’s penchant for keeping large data sets relating to their employees, patients, or customers’ history in assorted storage systems has raised several privacy concerns and the potential damages that could set a company back.
Predictive Analytics
Health treatment traditionally has been reactive to an individual’s explicit request or symptoms. Many long-term conditions arise from biomechanical, environmental, occupational, and lifestyle determinants whose first impact may precede the appearance of clinical issues by many years. Predictive analytics is the science of leveraging historical data to reveal patterns to forecast the future based on the tested assumption that if we look at what is happening now, we will know what will happen in the future. Typically, methods used in predictive analyses rely on one of the following modeling choices: expert forecasts that are then subjected to a weights inference; behavioral forecasts; reduction of the system into the two-state system; analysis of all previous data to select the best representation of the underlying data.
Given their capability to scan historical databases to identify individuals or clusters of specialty care that present high chances of adverse future occurrences, predictive algorithms are powerful tools in evolving prevention strategies and improving modeling outcomes. For example, models predicting first hospital admission or future ischemic events have identified individuals previously considered to be at low risk and often overlooked by prediction models. Identifying at-risk populations sets the stage for effective triage. The second major advantage of prediction models is their potential to generate personalized and proactive intervention. Machine learning may be better suited than risk conditions. High model accuracy pertaining to hospital readmissions for chronic diseases or repeat myocardial infarction indicates that different predictive models for individuals with or without these conditions will be necessary. Research in AI and ML is expected as it evolves to strategies for predicting risk and making decisions. AI/ML could play a significant role in this area by helping in the early identification of patients at risk for future diseases, leading to new preventive care strategies. This would mean a fundamental shift away from the reactive disease diagnosis strategy to preventing or reducing the progression of diseases to a clinical stage. PLAIN LANGUAGE SUMMARY: This section provides an understanding of predictive analytics. Predictive analytics can empower medical professionals by identifying the patients who may need extra care and assistance from a very large healthcare population, allowing for early intervention or prevention. However, patient data, and even more so outcome data, are not always accurate. Always bear in mind when collecting patient data that one patient’s case is a small proportion of a very large group and will never tell the whole story.
Patient-Centered Care Models
Innovations in patient care are moving away from the old health care adage that says, “The patient will come when he is sick.” Instead, the strong emphasis is now on patient-centered care, which has the following characteristics: care is designed to meet the needs, preferences, and values of patients and their families; care has a positive effect on patients; small and large communities and society benefit from the introduction of these new services or care models; the service or care delivery process is rooted in a strong evidence base; the care and services are also effective and efficient. Patient-centered care, therefore, aims to better embody the principle of putting the user at the center of services by co-designing care to meet individuals’ unique needs. This shift reflects a broader movement away from provider-centered health care towards a patient model which seeks to empower individuals, drawing on their strengths and resources. There are a number of different frameworks that posit ways in which health care professionals communicate with patients as partners in their care. For example, explanations have been given of shared or collaborative decision-making, which allows service users to be involved in decisions about their care, or the multidisciplinary development of care plans, which are individualized and devised with the recipient of care’s input. Definitions of patient-centered care usually emphasize the importance of practitioner-patient interaction. Fundamental to these definitions are key principles like: treating patients with respect and empathy, asking and responding to the patient’s personal and emotional needs, not mindlessly approving patients’ requests, and, fundamentally, supporting patients in self-care. A busy clinician may be reluctant to engage a patient’s participation in decision-making, simply to avoid “lengthening the encounter” by increasing the patient’s talk time. In such situations, doctors are prone to using shortcuts such as the “dominance approach”: they can be controlling or directive and tell the patient what to do, or use a doctor-centered involvement style. An extreme example of a doctor-controlled decision would be failing to offer different treatment options at times, particularly when the treatment choice concerns the patient directly. Conversely, several studies show that patients who are involved report a higher adherence to the treatment. Involving patients in care planning can lead to improvements in their skills, satisfaction, quality of life, and their ability to self-care. Many organizations and bodies currently promote different mantras and means through which these results are achieved. Formally, care and treatment planning is upheld as a panacea to the ills of a business-oriented, provider-sided public service and as the instrument that redefines service user and practitioner interactions. The expression ‘care planning’ is often used explicitly as a ‘strength-based’ practice where health and care professionals assist people to make informed choices about the treatments and care that are made available to them. It sounds lovely. It’s the kind of thing one stands up and applauds. It’s care, right? Indeed so, but have clever programs and policy agendas given rise to such commendable services, or is the space on this plane we call “health and care” really so easily navigated? The successes arising from care and treatment planning approaches are impressive. Research shows that people who are given choice about their treatments and care are more likely to self-report that they are engaged and happy with services. They are also more likely to report improved health and to have beneficial outcomes. Some staff members also believe that care and treatment planning can lead to financial savings for their services by reducing the duplication of services and “unsuitable outcomes.”
Personalized Medicine
Individualized, individual-oriented, patient-centered, or personalized medicine is rapidly becoming a keystone of the current era of consumer and patient-driven health care. Although this realm conveys a genetic and genomic connotation that encompasses how differences in an individual’s genetic makeup can result in different diseases or conditions, it also includes how those varying traits manifest differently in an individual due to the proteomic and metabolomic factors as well as environmental and lifestyle contributors. The conglomeration of all such parameters is then utilized to segregate individuals into distinct treatment plans. This is especially powerful in conditions where a genetic component is quite strong. The underlying premise in this regard is, “one size does not fit all.” For ignored is the genetic/environment paradox in personalized medicine; healthcare will remain best treating generalizations.
Personalized treatments are designed for a select subgroup of patients. This approach premeditates increased effectiveness of the therapeutic with a concomitant reduction in adverse effects. Recent advances in genomics, biotechnology, and bioinformatics have allowed many more drugs to be approved in conjunction with such tests than were thought of before. Favorable molecular results associated with personalized medicine are seen in the oncology arena as well as in the treatment of chronic diseases. Healthy genetic volunteers could now be tested at birth, decreasing lifetime health care costs and increasing outcomes. Discussion has also focused on ethical concerns regarding genetic testing and how information from this testing is going to be both stored and understood by health care practitioners. How this knowledge is presented to consumers will be a new focus of research in the near future. It must be emphasized that, as a society, there must be an obligation to use knowledge responsibly, and each patient must be treated with the utmost respect, ensuring that ethical standards are met in our data-driven world, no matter how sterile the data may seem to be of humanistic pathways. Modern health care as a trend is heading more toward the validation of disease outcomes via biomarkers, and drug therapy becomes an afterthought. An army of researchers around the world is focused on the individualization of treatments. Such is our way, a testament to the shift in health care from disease treatment to health promotion and disease prevention.
Challenges and Future Directions in Health Care Solutions
Despite the potential benefits of innovative health care solutions, barriers to implementation and sustainability exist, including issues related to funding, commercial enterprise, workforce education, technology adoption, understanding of solutions, usability, local demand, and demand aggregation. The complexity of relational, technical, organizational, and financial systems in health care may also restrict the potential impact of solutions. Health care systems are often very slow in adopting new methods in practice. Broad stakeholder and community-framed priorities are often overlooked in favor of incentives for existing health systems to change. Coordination between and scaling up of integrated services, rather than single innovative solutions.
Progress in health care, however, continues, often driven by a collaboration of providers, patients, and other stakeholders. There is a range of innovative health care solutions and models of care emerging that could potentially be implemented for larger numbers of people. They span a wide range of clinical areas and models of care, use a mix of emerging technologies such as education, best practice communication, assistive, digital, and social communication, and can vary in sophistication. Most health systems are unlikely to be able to afford the setup, service delivery, or financial subsidy costs over time, so it is necessary to rethink the economy of health care. Patients demand care of a type and quality unimaginable 50 years ago and are also able and willing to forge their own solutions. Electronic and mobile technologies being developed allow for innovative health care solutions to be delivered directly to patients, thus both empowering and expecting patients to self-manage. It is important for the provision of cost-effective person-centered services and avoids unnecessary medical interventions. The only drivers of interest are innovative entrepreneurs with new ideas, skill sets, and processes. As people age, the number of non-communicable diseases they live with increases, and the capacity of health care systems to help is limited; economic viability is a problem.
To ensure that we are able to navigate future challenges and health inequities and be mindful of relevant ethical considerations, strategic planning and ongoing evaluations are essential. Efficient adoption and diffusion of innovative health care solutions depend on numerous factors, including targeted activities, successful partnerships, effective models, adaptability, responsiveness of services and the wider environment, and striking the right balance between evidence and innovation in a rapidly evolving health care system. Researchers, however, argue that this is not as easy and simple as making sure innovative health care solutions are used and learning from evaluations, maintenance models, or finalization of projects. Adaptability and responsiveness, training, culture change, patient involvement, and broader social networks are the directions for the future of health care solutions. New models of care, faster alternatives, or creativity could be part of the solution.
Ethical Considerations in Health Care Innovation
Health care presents fertile ground for ethical considerations to play out. The doctor-patient relationship inherently involves power that compels careful ethical review. As new technologies that can diagnose and treat illness in increasingly innovative and efficient ways proliferate, it is essential that their development and application be guided by ethical consideration and decision-making. Questions about patient consent, especially regarding the use of AI and the analysis of increasingly large amounts of data, abound. Considerations of patient autonomy and justice, particularly in the most equitable way to distribute resources, are central to ethical decision-making in the development of new technologies in health care. It is also critical to consider that when patients consent to share their data, they often must consent to a longer chain of data sharing, collection, and usage.
A significant issue to consider in a discussion about the ethical questions relating to health care innovation is whether the data are allowed to be used, if that data is owned by a profit-based industry and, quite possibly for this reason, might not be reliable. When big pharma stepped into mental health care, the involvement of big tech has already taken place in the form of wearables, mobile apps, and general data aggregation. With this influx comes the risk of bias, the specter of discrimination, as well as the possibility of manipulative design. Ways in which to ensure that health care technology innovators and providers are being as ethical as possible and the manner in which technology can be safeguarded are discussed. Particular attention is paid to frameworks put in place to ensure ethics is at the forefront of the health care system and that regulatory bodies charged with controlling health care and innovators are doing their job. These issues were further highlighted when the Canadian government introduced a proposed new Consumer Privacy Protection Act to replace existing legislation. The purpose of this act is to provide better protection to Canadians who, in an increasingly digital age, must place unwavering confidence and trust when transferring and storing their sensitive personal health and other information to product and service providers. In a statement when introducing the proposed legislation, the Minister of Industry emphasized that regulators and health care technology service providers, including app developers and website owners, will need to put in place privacy management programs that operate in a highly effective and accountable way. They would have to ensure that the privacy of Canadians is protected and that they are accountable in the event of problems. They would have to get express consent from individuals if sensitive data or children’s data was collected or used, or even disclosed. Clearly, patients recording health information in a digital environment want to be assured that the data is well protected and not sold to third-party organizations producing additional income streams for those who innovate digital health care services and products. Given that billions of dollars can be on the table, the ability to obtain free and informed patient consent from those who have not only reported their health information in a digital forum but have also given permission for third-party sharing becomes especially critical because data is often sold to third-party, indirect commercial stakeholders.
