Influence of Predictive Analytics on Medical Industry
概观:
Predictive Analytics, it is being said, is going to redefine how healthcare is delivered. It will predict occurrences of critical illnesses and probability of readmissions in the future. Other sectors such as food and beverages, 出版物和娱乐活动已从使用Predictive Analytics获得收益,,en,没有理由医疗保健不能做同样的事情,,en,必须首先在医疗保健方面首先了解Predictive Analytics的定义和范围,,en,千篇一律的模型无法正常工作,,en,同样重要的是，提供用于交付分析的基础架构，并且能够以正确的格式向医疗保健专业人员交付所需的信息。,,en,提供正确和主动的医疗保健,,en,需要为医疗保健专业人员提供正确的上下文和元数据,,en,而Predictive Analytics对医疗保健有好处,,en,必须首先对其进行自定义，并且必须以正确的格式提供正确的数据,,en,什么是预测分析,,en. There is no reason healthcare cannot do the same. 但, the definition and scope of Predictive Analytics needs to be first understood purely in the context of healthcare. The one-size-fits-all model is not going to work. It is also important that the infrastructure for delivering analytics is provided and it is able to deliver the required information to the healthcare professionals in the right format. To deliver the right and proactive healthcare, healthcare professionals need to be given the right context and metadata. So, while Predictive Analytics is good for healthcare, it must first be customized and the right data in the right format must be delivered.
What is Predictive Analytics?
预测分析是高级分析的一个分支,,en,根据历史数据提供某些事件的预测,,en,数据模式和其他输入,,en,可以采取积极措施来满足预测产生的要求,,en,做出预测,,en,其他分支机构使用的预测分析杠杆技术，例如数据挖掘,,en,造型,,en,和统计并集成信息技术,,en,管理和建模业务流程,,en,这些预测可用于识别未来的风险和机遇,,en,预测分析可以帮助企业组织实现很多目标,,en,以下是一些示例,,en,识别隐藏的关联和模式,,en,提高客户保留率,,en,降低风险以最大程度地减少损失和风险,,en,提高客户满意度,,en analytics that provides predictions of certain events based on historical data, data patterns and other inputs. Proactive steps can be taken to address the requirements arising out of the predictions. To make the predictions, Predictive Analytics leverage techniques used in other branches such as data mining, artificial intelligence, modelling, machine learning and statistics and integrates information technology, management and modelling business processes. The predictions can be used to identify risks and opportunities in the future. Predictive Analytics can help business organizations to achieve a lot of things. A few examples are given below:
- Identify hidden associations and patterns.
- Improve customer retention.
- Reduce risk to minimize loss and exposure.
- Improve customer satisfaction.
有很多现实的例子，说明企业如何从预测分析中受益,,en,埃森哲进行了一项调查，以了解使用Predictive Analytics可以使不同的企业受益,,en,一些发现是,,en,百思买发现不到,,en,的客户促成了,,en,其销售,,en,然后，它按逻辑对客户进行细分，并重新设计商店和店内体验，以反映特定客户群体的购买习惯,,en,橄榄花园,,en,一家美国休闲餐厅使用数据来设计和重新设计菜单,,en,它可以大大减少食物浪费,,en,预测分析已应用于许多领域，例如医疗保健,,en,欺诈检测和风险管理,,en. Accenture conducted a survey to find out how different businesses have benefitted from using Predictive Analytics. Some of the findings are:
- Best Buy discovered that less than 7% of its customers contributed to 43% of its sales. It then segmented its customers logically and redesigned its stores and in-store experience to reflect with the buying habits of specific customer groups.
- Olive Garden, an American casual dining restaurant uses data to design and redesign its menu. That way, it has been able to cut down on food wastage significantly.
Predictive Analytics is being applied to a lot of domains such as healthcare, Customer Relationship Management (CRM) fraud detection and risk management.
Also read – Understanding Machine Learning
Predictive analytics definition in the context of healthcare industry
Theoretically, Predictive Analytics has a big role in improving healthcare. Although it is still a new entrant in healthcare management and its scope is still being worked out, Predictive Analytics can analyze historical patient data and provide predictions things like illness risks, probability score of heart, asthmatic attacks based on patient profile, probability of readmissions.
The human brain cannot deeply analyze more than 6 to 8 variables at a time to properly profile a problem. But, the algorithm of a predictive model can analyze hundreds of variables at a time to create an accurate profile of a medical problem. Based on the profile, accurate diagnosis and risk predictions, if any, can be made.
Predictive Modelling can help control costs towards medical care. In the US, 1 in 5 Medicare patients are readmitted to the hospital within 30 days of discharge which results in an expense of $17 billion a year.
Also read – What is Internet of Everything (IoE)?
Role of Predictive Analytics in healthcare
Simply put, predictive analytics can play an important part in delivering healthcare that is based on prevention and proactive actions. Healthcare costs account for a substantial chunk of the GDP in the US. Healthcare costs in the US are 17.6% of the GDP, $600 billion more than the ideal healthcare expenditure considering the population, per capita income and economy of the US. Readmissions and critical illnesses such as heart attacks, stroke, diabetes and kidney problems account for a big chunk of the healthcare costs. 借助数据模式分析及其后的分析，可以更好地预防或管理一定比例的医院再住院和重病复发,,en,下面描述的案例研究为预测分析如何改变医疗行业提供了一个范例,,en,Steadman Hawkins诊所提高了盈利能力,,en,要求,,en,卡罗来纳州Steadman Hawkins诊所的主要目标是通过将现有医生的服务与诸如药房等辅助服务进行最佳组合来提高盈利能力,,en,抽血疗法和物理疗法,,en,诊所希望根据需求高峰时段或季节来优化可用性，以免失去赚取收入的机会,,en.
Must read – How Big Data Can Revolutionize Healthcare Sector?
实例探究
The case studies described below set an example of how predictive analytics could transform healthcare industry.
案例分析 1: Steadman Hawkins Clinic improving their profitability
The requirement
The main objective of the Steadman Hawkins Clinic of the Carolinas was to improve profitability by optimally combining the services of available physicians with the ancillary services such as pharmacy, phlebotomist and physical therapy. The clinic wanted to optimize the availability depending on the peak demand hours or seasons so that no opportunity to earn revenue was lost. For example, 哮喘发作可能在两个季节之间的过渡期达到高峰,,en,那是最需要肺科医师和其他技术人员的时候,,en,那个行动,,en,Steadman Hawkins诊所与River Logic合作,,en,实施预测分析的分析公司,,en,考虑了所有数据点和约束条件，以确定设计诊所设施和所需人员的最佳方法,,en,结果,,en,Steadman Hawkins诊所能够通过以下方式提高其净利润,,en,一年一百万,,en,他们还能够从以下方面提高财务预测的准确性：,,en,无名诊所提高了盈利能力,,en,诊所希望通过最佳地利用包括员工在内的资源来改善对患者的服务并提高其盈利能力,,en,设施和仪器,,en. That is when pulmonologists and other technicians are in demand the most.
The action
Steadman Hawkins Clinic partnered with River Logic, an analytics firm that implemented predictive analytics. All data points and constraints were taken to determine the optimal way to design the clinic facility and the staff required.
The results
Steadman Hawkins Clinic was able to increase their net profitability by $20 million a year. They were also able to improve the accuracy of their financial predictions from 30% to 32%.
案例分析 2: Unnamed clinic improving their profitability
The requirement
The clinic wanted to both improve services to the patients and improve their profitability by optimally using their resources which includes staff, facilities and instruments.
The action
The clinic collected copious data on different variables such as type of care needed by patients, staff profile and qualification, patient profile, quality of services delivered such as response time, outcome, patient experience and wait time for patients. Based on the data collected, predictive analytics was put to use. They expected concrete analytics and course of actions to put in use.
The result
Through the clinic is still putting in use the predictive analytics, there are signs that they are on course to achieve at least 10% profitability.
Also read – The Escalating Scope of Big Data Analytics
Important points to remember
It is not that implementing predictive analytics will start doing wonders right away. The results depend on the approach. First, the industry needs to determine what predictive analytics mean in its context and then specify its scope. Also, the healthcare industry needs to remember the following lessons from other industries.
- The amount of insights is not directly proportionate to the amount of data. You are not going to get more insights just by increasing data collection.
- Insights do not necessarily provide value. You need to first customize the insights in your context so that it becomes useful.
- Implementation of predictive analytics is going to be a big challenge. You need to embrace the right technologies and deliver insights to the healthcare professional in the right format.
Must read- Analytics of things in the context of IoT
Explore more interesting articles on Analytics
Summary
Predictive Analytics needs to be married with Prescriptive Analytics to deliver the right results because the industry needs not only the predictions but also the course of actions. While the concept seems to be rewarding in the end, businesses need to make the right investments and be patient with the results to reap the benefits.
