Traditional healthcare in digital era
Around the world, we are seeing stagnating birth rates and increasing life expectancies. WHO reported in 2017 that by 2050, this will cause the number of people aged 60 or over to more than double - from 900 million to 2 billion, representing an increase from 12% to 22% of the population. As this large percentage of our population ages, there will be an increased need for more care coverage to address growing eldercare needs. At the same time, as populations age and health needs become more complex, providers are facing a global shortage of workers at the worst possible time. WHO mentioned that by 2030, providers will suffer from a projected shortage of 14 million workers worldwide. For an instance, in China, Health System burdened by an aging population and prevalence of chronic diseases resulting from poor lifestyles. Patients' deductible costs have been growing 8x faster than wages and many families are impoverished by the disease. Patients are frustrated with the managed care model and want more accountability, pricing transparency and tech-enabled convenience in managing their healthcare. As we saw, the traditional medical platform, the combination of hospitals and doctors, is facing uneven distribution of medical resources and medical shortage among regions.
In digital economy, just as digital transformation in other industries has led to increased patient expectations, patients have higher expectations than ever towards their doctors, insurers and the treatments they receive. A survey of TranscendInsights in 2017 found that 87% of patients now expect any health institution to have full access to their medical history. To live up to increased patient expectations and best serve patients, health industries will have to reconcile more extensive networks of data with increased security efforts. This is the best time for the development of a new digital diagnosis and treatment platform, which will reshape the health platform and improve the efficiency of diagnosis and treatment by optimizing resources such as hospitals, insurance, doctors, patients and medical equipment. The new digital treatment platform needs to quickly assemble both sides of the platform -- doctors and patients -- via the Internet and unbundle doctors from the hospital platform. This creates a bilateral market based on doctors and patients. At the same time, the medical equipment and the hospital need to be unchained. The owner of the medical equipment can not only provide the use right of the equipment to the hospital, but also provide the equipment in the idle period to the digital diagnosis and treatment platform to optimize the utilization rate of the equipment. On the other hand, from the perspective of patients, data between traditional medical platforms is not mobile. In China, for example, it is difficult for patients from one hospital to see their records from another. Not to mention across regions and even countries. Fundamentally, this is an issue of trust and accountability, which is very detrimental to patient access to care and adds to the high cost of testing. These are all problems that digital diagnosis and treatment platforms can help solve.
Big data in digital diagnosis and treatment platform
According to research compiled by Datavant , over 4 trillion gigabytes of healthcare data is generated annually, and this is projected to double every two years. Data from Accenture estimates AI in healthcare will be a $6.6 billion market by 2021. AI startups have raised $4.3 billion across 576 deals in the last six years, and healthcare providers are projected to save almost $150 billion by 2026 with the help of AIs that can prevent medication dosing errors. The future of AI healthcare technologies is looking bright. The big data model of digital diagnosis and treatment platforms uses real-time data and analysis to realize predictive and prescribed analysis methods. This means that analytics and artificial intelligence (AI) provide the ability to perceive the medical world, understand, act and learn. AI uses machine learning to provide the ability to mimic human behavior and performance, ultimately improving patient diagnosis and outcomes. The platform's artificial intelligence system collects and processes large amounts of data in real time to identify patterns. The platform then USES this information to automate and simplify the process. Interrogation analysis includes the process of predictive analysis to process patient data and discover insights, suggest actions, identify correlations, link symptoms to disease, and suggest treatment options.
Digitizing personal medical records is one of the platform's greatest strengths. Constantinides, Henfridsson and Parker argue that platform and big data are new infrastructure in digital era. Platform and big data ability to collect many unique data types (qualification, medical, Rx, laboratory, biostatistics, HRA, health, etc.) enables medical stakeholders to quantify information and enable The resulting data is more accessible. Authorized departments can more easily transfer data and generate detailed reports and analyses. Collecting this available data in an organized and systematic way is the first step in data analysis. Descriptive analysis allows providers and other key stakeholders to better understand facts, including health history, costs, and demographics. When health care providers can identify people who are consuming more resources, they can begin developing health management procedures to improve clinical treatment. Descriptive analysis suggests possible outcomes and results of actions that may maximize key business indicators. It enables providers to provide more accurate and personalized care treatments. A detailed description of the patient makes it easier to predict the response to a particular treatment.
Predictive analysis of healthcare is equally important for individuals and government public health agencies. Brynjolfsson and Mcafee mentioned that the development of hardware computing power and algorithms has accelerated machine learning into all industries. This is particularly true in the predictive analysis of the healthcare industry. Healthcare predictive analytics includes the use of all types of data, statistical processes, and machine learning techniques to identify and provide probabilistic estimates of future outcomes based on the data. Predictive analytics plays a huge role in helping individuals prevent disease and helping healthcare organizations reduce costs, and it enables health care organizations to build and evaluate models that support preventive care. Medical predictive analytics allows platforms and physicians to identify and classify patients based on major health conditions. For example, patients at risk for a heart attack can be notified of threats in a timely manner, making it possible for patients at risk for heart attack to receive timely care and provide personalized care for specific diseases. Another important use of medical predictive analytics is to create a platform for demand forecasting for the supply of medical resources. If the platform has reliable and accurate data, it will be easier to estimate and forecast demand and supply and shortages in advance. This saves time and money.
Using cost-based big data analysis to optimize expensive medical device resources, cost-based analysis of digital diagnosis and treatment platforms can help the medical industry ensure that more costly medical resources are supplied in a more efficient and cost-effective manner. The use of aggregated data provides visibility into utilization and spending, and enables equipment owners to make more informed decisions and actions. This insight is critical to creating a more efficient healthcare supply chain. It is important for medical resource providers to have a patient allocation model that enables them to order the exact number of supplies they need. This is especially important for offline clinics with limited storage space. Supply chain costs are the second largest expenditure of traditional medical systems in addition to labor costs. Traditional hospital and medical supply chain models are fragmented. A core purpose of the digital diagnosis and treatment platform is to precisely match medical resources. In an ideal situation, hospitals and other suppliers always have sufficient supplies to meet demand without excessive inventory. Therefore, the society will no longer have shortages of medicines and materials, and natural disasters will cause serious shortages of medical resources.
Blockchain in digital diagnosis and treatment platform
The Gartner indicates that 7% of government organizations have already deployed blockchain/distributed ledger technology, or plan to deploy it within 12 months. In the same survey, 43% of government respondents indicated they had no interest in blockchain, up from 35% in 2018. This waning interest likely reflects the growing awareness on the part of government CIOs that challenges posed by blockchain outweigh potential benefits, at present. These challenges include scalability, lack of international data standards, insufficient attention devoted to governance of large-scale networks of participants and/or immature technology not ready for deployment at scale. In China, government investment in blockchain is even more aggressive. However, the medical industry still has huge data sharing problems. In most cases, patient data cannot be shared between hospitals and regions due to legal regulations or competition between public and private hospitals. One of the key factors is the lack of trust between the hospital and the hospital. The digital diagnosis and treatment platform use blockchain technology, and it can be said that medical examination equipment providers, doctors and patients are connected in series through distributed ledgers. Give the patient a complete and credible electronic lifetime record. Hospitals use credit tokens to access patient data. Research institutions can also use cryptocurrencies or tokens to access desensitized patient treatment data for research purposes.
In addition to the benefits of patient data consistency, the recording and review of compliance with doctors and medical practices is also critical. Blockchain helps prevent tampering and misuse of medical records. The ability to comply with regulations helps to proactively identify regulatory risks and workflow efficiencies across the entire healthcare provider organization, such as misuse of patient data or theft of controlled substances. In the Medicaid ecosystem, such as China's basic medical insurance and commercial medical insurance, quality assessment and compliance with evidence-based treatment, and assessing the outcome of diagnosis and treatment are also very important. Using blockchain to track the quality of diagnosis and treatment helps to strengthen medical accountability and complete the platform's recommendation system and reputation system at multiple levels. Blockchain tracking methods can also be used to track various activities and health care results for specific populations.
Building trust and user satisfaction is always the development goal of digital diagnosis and treatment platform. If we combine big data with block chain technology, we can also redesign or change the traditional workflow to increase liquidity and release capital. Help reduce infrastructure costs, increase transparency, reduce fraud, and improve execution and billing times. And it can provide patients with more effective health care to reduce costs and improve patient satisfaction. By helping health care providers identify high-risk groups, for example, potential patients at risk for lifestyle factors such as diabetes, depression, hypertension, and cardiovascular disease, analytical tools can provide these people with the advantages of adequate interventions and reduced costs, such as preventive care.
The Underlying Logic of the Digital Platform
The digital diagnosis and treatment platform is a typical multi-sided market, which links the multiple sides of the market together to make efficient matching and profit from it. The network effect of the platform is the key to the success of the platform. Catherine introduced that network effects require an increase in supply on one side of the network to drive revenue on the other. From this perspective, the acquisition of new platform users is as important as the retention of old users. For the acquisition strategy of new users, the problem of chickens and eggs can first be broken by subsidizing one side of the platform. The digital diagnosis and treatment platform can start by subsidizing patients, set the cost of patient participation in the consultation to zero, and even reduce the cost of examinations to zero in the early stage to attract patients. In the early stage, it can also increase the remuneration for doctors to provide diagnostic services to attract more doctors to participate. For medical equipment providers, the use of idle equipment will also increase the motivation of medical equipment providers. Then, from the perspective of promotion and dissemination, a community of specialist patients can be established, and a mechanism for recommending patients by specialists can be used to quickly promote the popularity and dissemination of the platform in the community. Finally, a prestige system for doctors should be established to separate doctors' skill levels from the traditional hospital political system and establish a more neutral and decentralized mechanism.
From the perspective of user retention, allowing patients to receive accurate treatment and quality services is a prerequisite to prevent user loss. This requires that on the doctor's side, through the big data algorithm, establish a good recommendation system and reputation system, and push the high-quality medical resources to the patient side with reasonable pricing. When the platform traffic reaches a certain level, it is necessary to discuss more competitive pricing with medical equipment providers and drug providers to maintain the platform's competitiveness. It is also important to keep the balance between new user acquisition and old user retention.
The medical industry is an industry with very high legal regulations. Athey, Catalini and Tucker discussed that when faced with incentives, people are willing to sacrifice a small portion of privacy. Therefore, the platform needs to consider how to balance policy and management when promoting customers. First, the government's development direction and related regulatory requirements in the medical and health industry need to be clarified. Patient privacy protection, platform data management, and industry policy requirements need to be very complete. Secondly, medical disputes caused on the platform also need to be taken very seriously. Doctors' prescription tracking, artificial intelligence pre-review and early warning, and patient medication data should form a closed loop for final decision. Finally, the positioning of the digital diagnosis and treatment platform is to strip the capabilities of the outpatient department of the hospital, but for the patient's admission and treatment, the platform also needs to consider the data sharing and process docking with the existing medical information system. Through the digitization of the diagnosis and treatment process, existing medical resources will be optimized to improve the operating efficiency of the hospital, reduce the capital and time costs of patients, and bring more benefits to the doctor group and medical equipment providers.