普华永道的一项有见地的调查揭示了s that 91% of surveyedpatients underwent video virtual care and are likely to use it again. In addition, 66% of all individuals would use remote clinical trials. Thus, it’s evident that the healthcare world is becoming used to technologies and using them to their benefit.
Given the complex nature of the healthcare world, medical facilities need powerful software and in-depth databases. Today, organizations acquire cutting-edge technology to enhance performance and productivity.
It also means that technical devices and software’s gather increases volumes of patient and doctor data- which translates to a dire need for big analytics in the healthcare industry.
大数据已成功改变了医疗专家如何管理，运行和分析数据。在这里，我们讨论了大数据及其应用程序的重要性：
A Quick Glance at Big Data in Healthcare
根据一项深入的研究，大数据可能比其他行业更快 - 在2020年至2025年之间，年龄增长率为23％。
Another insightful research reveals that the big data analytics industry will likely reach $67.82 billion by 2025. It indicates an impressive growth rate of 19.1% between 2018 and 2025.
更重要的是，麦肯锡和公司的研究表明，大数据可以帮助个人每年节省yabo.com下载高达300至4500亿美元。
Thus, it’s evident that big data is transforming the healthcare industry, boosting productivity, and improving value-based care.
What is Big Data In Healthcare?
大数据是指从各种来源积累的数据量。大数据是指在医疗保健领域中的电子健康记录，基因组测序，医学成像，付款人记录，可穿戴医疗设备等等。
Users often confuse big data with traditional electronic medical health data. Here’s what makes big data stand out:
- It helps analyze high volumes
- It moves at an incredible velocity
- It is highly variable since it derives from various sources
Types of Healthcare Data
Healthcare data collection is of various types, including:
1. Administrative Data
When providing top-notch medical services, institutions need to collect data about the most profitable services.
Healthcare providers may collect such information by pulling data from individual patients, including their appointments, registration in programs, billing, and more.
Healthcare organizations can allocate their resources and improve value-based care armed with this information.
2. Electronic Health Records
电子健康记录包括与患者病史有关的机密数据。它包括他们过去的疾病，治疗计划，结果等。
Doctors and staff members create EHRs and update them according to need. It helps diagnose, develop treatment plans, and transmit data from one specialist to another.
3. Clinical Data
养老院等医疗机构必须提供标准化的医疗保健数据。医疗机构可以确定使用此数据分配资源的最佳方法。
Big data analytics also allows healthcare institutions to assess the performance and productivity of a company.
大数据在医疗保健中的应用
Up to 97.2% of all organizations invest in big data and AI. Why? It’s because big data offers users a plethora of benefits and uses, including:
1. Improve Health Tracking
Big data analytics and IoT offer new and innovative ways for tracking user statistics and vitals. Conventional wearables that can detect your heart rate, sleep time, beats per minute, distance walked, and more
Apart from them, revolutionary medical equipment can now track glucose levels, blood pressure, pulse oximeters, and more. With these state-of-the-art techs, healthcare providers can monitor the health and safety of patients without keeping them in hospitals. They can also identify potential risks and intervene before the situation worsens.
2. Streamline Hospital Administration
大数据有助于简化和抚平医疗administration. It achieves this by effectively reducing the cost of patient care, providing incredible clinical support, and managing the number of at-risk patients.
它还允许医疗保健提供者分析来自各种来源的数据。反过来，医疗保健提供者可以得出结论最佳治疗计划及其对特定患者健康的影响。
3. Integrate Big Data with Medical Imaging
Medical imaging is an essential part of the healthcare world- mainly since specialists perform more than 600 million imaging procedures annually.
Analyzing and storing these images can be expensive and time-consuming since radiologists examine each image individually.
Big data analytics solve this problem by efficiently analyzing thousands of images and converting them into numbers. This way, big data can help streamline workflow within medical institutions.
4. Eliminate Prescription Errors
Research by the Network for Excellence in Health Innovation reveals that prescription errors cost healthcare institutions approximately $21 billion annually. In addition, it affects 7 million patients and causes more than 7,000 deaths.
大数据有助于减少处方错误，从而提高结果并实时保存。
5.降低总成本
大数据是降低经历或不适当的医疗机构成本的绝佳方法。Big Data的预测性分析性质通过有效预测入院率来解决此问题。
Moreover, the insurance company can save costs by providing wearables and health trackers. This way, patients don’t have to stay in hospitals for prolonged periods, which helps reduce wait times for other consumers.
6.用大数据与癌症作斗争
Cancer rates are increasing dramatically across the world. But big data can help combat cancer effectively and appropriately.
With big data and its analytical tools, healthcare providers can effectively detect and diagnose diseases in their early stages. In addition, they can curate holistic treatment plans based on a patient’s unique genetic makeup and minimize side effects to maximize effectiveness.
It also provides better support for mapping billions of DNA base pairs.
7. Prevent Suicide and Self-Harm Attempts
每年,大约有800000人提交隋cide which translates to one death per 40 seconds. In addition, more than 17% of the world’s population self-harms during their lifetime. While these statistics are alarming, they’re only growing.
自杀和自我伤害是要解决的复杂领域。但是，大数据步骤是帮助做出积极改变的理想解决方案。大数据分析有助于确定哪些人更有可能自我伤害或自杀。
In an insightful study by KP and the Mental Health Research, researchers provided individuals with a mix of standard depression questionnaires and EHR data. With this data, researchers were able to identify people at a greater risk of attempting suicide and self-harm.
8. Facilitate Telemedicine
Integrating big data with telemedicineposes several advantages, including allowing doctors to perform operations from miles away.
大数据还促进了早期诊断，虚拟护理援助，远程患者监测等等。远程医疗和大数据使患者和医生的生活更加轻松：
- 医生不必填写不必要的文书工作和咨询
- Healthcare experts can monitor and consult 24/7
- Patients help reduce waiting in lines
- Reduce unnecessary hospitalizations and re-admissions
- Clinicians can effectively predict medical events at the right time
- Effectively reduce costs and boost the quality of service
9. Facilitate Preventive Care
Preventing diseases is far better than curing them for hospitals, patients, and insurance companies.
有了大数据，医疗保健提供者可以有效预测某人根据当前的健康指标和行为而生病的机会。
In this way, doctors can identify warning signs before symptoms evolve into a severe illness.
10.防止美国滥用阿片类药物
Studies reveal that overdoses from opioids cause more accidental deaths than road accidents—big data analytics in healthcare steps in as the ideal solution.
With big data, healthcare organizations can determine, with high accuracy, which patients are at risk for abusing opioids.
11. Enhance the Use of Electronic Health Records
Electronic Health Records提供医疗组织大型dat的访问a, meaning most healthcare institutions are already using them. According to insightful research, more than 94% of all U.S. hospitals leverage EHRs to execute healthcare practices.
电子健康记录为医生和患者提供了完整的患者病史。由于文档是通过尖端信息系统共享的，因此所有患者doctor数据都是安全的。
EHRs also trigger warnings when a patient doesn’t follow the doctor’s orders and reminders when a patient should get tested.
12. Improve Supply Chain Management
Weak and fragmented supply chains can impact all components of a medical institution, including patient care, long-term finances, patient care, and more. Luckily, big data in healthcare leverages insightful analytics to streamline the supply chain and ensure efficiency.
通过利用分析工具，医疗保健提供者可以跟踪供应链绩效指标。这样，医疗机构可以做出数据驱动和准确的决策，并每年节省多达1000万美元。
With predictive and descriptive models, you can enhance decision-making skills to reduce supply variations, negotiate prices, and optimize the overall order process. Ultimately, HCOs can thrive while delivering unparalleled customer service without delays or snags.
13. Develop New Therapies and Innovations
Extensive data analysis promises a bright future in the medical industry by facilitating the discovery of innovative drugs and new therapies.
By combining historical and real-time metrics with data visualization techniques, medical institutions can identify strengths and weaknesses in processes.
In addition, with the help of knowledge-based genetic analysis and reactionary predictions, big data analytics can play a pivotal role in developing groundbreaking drugs and therapies
Overall, data analytics can help innovate, streamline, and enhance security, give confidence, and save lives.
14. Detect and Prevent Fraud
根据research, up to 93% of healthcare organizations experience data breaches. Big data is a valuable tool for preventing various errors in the healthcare administration, including wrong medicines, faulty devices, wrong dosages, and more.
It also helps prevent potential fraudulent insurances claims.
Why is Big Data Important in Healthcare?
Here’s a glance at the importance of big data in healthcare:
- 更好的客户服务
- Cost reduction
- Detects diseases at the right time
- Ensure accurate treatment
- Facilitates real-time alerting
- 预测特定治疗的风险
- Personalize patient care and treatment plans
- Predict the cost of treatment
- Prevents mass diseases
- Reduces medical errors
- Streamlined hospital operations
- Suicide and self-harm prevention
现在，让我们讨论医疗保健中大数据的主要好处：
1.增强患者护理
基于知识的大数据为医疗保健提供者提供了更有价值的临床见解。在这些尖端分析的帮助下，专家可以有效地改善患者护理。
A doctor can use healthcare databases to prescribe better treatments, make accurate clinical decisions, diagnose appropriately, and eliminate ambiguity.
With big data on our side, the healthcare industry is slowly moving closer to enhancing patient outcomes.
2. Efficiently Predict Patients at High Risk
Big data facilitates aggregate data which, in turn, provides predictive analytics helpful for pinpointing which patients are at a higher risk for experiencing particular diseases.
有了这些有价值的提示，医疗保健提供者可以在正确的时间进行干预以保护它们。这对于发现慢性疾病至关重要。
为了预测分析，医疗保健提供者需要汇总与各种因素有关的数据，包括患者的病史，社会经济概况数据，人口统计领域数据，患者的合并症等等。
3. Reduce Overall Healthcare Costs
Big data provides more significant insights and analytics, translating to more efficient patient care. It also ensures shorter hospital stays, fewer admissions and eliminates the need for re-admission. Ultimately, it helps patients save costs due to less hospitalization.
此外，预测分析可以帮助医疗保健提供者估计个体患者成本，从而通过有效制定整体治疗计划来最大化患者护理。
4. Improved Patient Diagnostics with EHRs
Electronic Health Records (EHRs) are typical applications for data sets and are crucial for achieving an efficient patient diagnosis.
EHRs comprise in-depth patient history, including allergies, medical history, family history, a treatment plan of past illness, previous diagnoses, and more.
EHRs make it easy for doctors to access patient files, update them with new information about illness or treatment plans, and notify patients about appointments.
5. Deliver Better Patient Insights
Analyzing big data helps you identify which segments are at a higher risk of getting infected or experiencing a particular disease. In this way, healthcare providers can take appropriate preventive measures at the right time.
此外，这些有见地的数据可以帮助病人告知和教育他们的福祉。它还有助于提高治疗计划的有效性，以提供更好的患者护理结果。
Future Trends for Big Data in Healthcare
Big data helps analyze workforce needs, streamline patient care, and boost customer experience.
毫无疑问，大数据将继续扩展，对医疗保健界变得越来越重要。在这里，我们列出了医疗保健中大数据的主要趋势：
1. Expanding Categories
Typically, users categorize big data within the three V’s:
- 体积
- Velocity
- Variety
但是随着大数据扩大，这些类别已演变为五个，包括“价值”和“真实性”。我们在这里讨论了每个“ V”：
- 体积-In today’s tech-savvy world, there is more data than ever
- Velocity-医疗保健提供者不会仅从她那里获取信息；相反，他们还使用应用程序，门户和其他各种快速高效的方法
- 种类-Healthcare organizations collect data from several different sources to create holistic plans
- Value-Refers to the result of the data a healthcare provider collects. It includes what benefits or uses the information brings to the industry or organization
- Veracity-Healthcare companies need to focus on increasing the trustworthiness and quality of data to ensure the security of health information and to produce better patient outcomes
2. Predictive Analytics
Another trend for big data in healthcare is leveraging predictive analytics to make the best treatment and diagnostic decisions. It also ensures significant cost-saving potential and improves outcomes for patients.
预测分析对临床医生，人力资源团队，金融部门以及医疗保健领域的每个人都有帮助。您的医疗保健公司可能yabo.com下载创建软件用于使用预测分析以：
- Determine which patients require re-admission
- Predict quarterly finances
- Identify sepsis rates
- Track which patients require additional care
- Track the well-being of nurses, doctors, and other staff members
- Create weekly or monthly schedules
Critical Challenges of Big Data in Healthcare
Let’s discuss the most pressing issues in healthcare:
1. Data Quality
Development of AI and ML algorithmsneed reliable input data free of inaccuracies to develop high-quality and trustworthy insights. If the data quality is poor, doctors may misdiagnose a patient or prescribe the wrong medicines.
It means that healthcare organizations need to work on data governance and data management to improve data quality effectively.
2. Management Issues
To fully benefit from big data analytics, organizations need to adjust how they do business. Data scientists need to connect with IT staff to run analytics effectively.
3. Data Mining
Data exploration tools used in Business Intelligence help solve data mining problems. Data engineers and data scientists can leverage data mining for improving healthcare services.
4. Policy and Process Challenges
Once you validate and aggregate data, healthcare organizations need to address various processes and policies.
根据HIPAA法规, medical institutions need to follow stringent guidelines to protect confidential health data.
The Bottom Line
大数据正在彻底改变医疗保健世界，这是一次治疗计划。但是，这仅仅是开始。大数据一定会带来切实的解决方案来改善基于价值的医疗保健，因此医学和融资的演变肯定会带来切实的解决方案。
Today, big data analytics empowers actionable insights on patients’ history, predicts overall health costs, reduces overall healthcare costs, and generates real-time alerting.