Data Analytics In Healthcare

Individual Pre-course Assignment

Ketan Patil_MBA

7/16/2015

Word count - 1877

Healthcare Industry

Introduction

Healthcare facilities all across the world are under great pressure to reduce its cost, enhance coordination in different practice areas and to deliver more value to patients. At the same time, industry is facing internal challenges such as inefficiencies in workflows and inability to deliver desired outcomes. Insights drawn from big data can help those organizations envision future prospective and improve outcomes.

Healthcare is one of the most data intensive industry. In the past decade, industry has experienced the shift in the horizon for the medical records as more and more pharmaceutical companies and other life science organizations started converting their data gathered over years into digitalised records and maintaining their data bases. Governments and other stakeholders in the healthcare industry are trying to increase the ease of the use of this data, to make access to this data easy and to draw actionable insights from this data in order to bring transparency into the field and increase efficiency in entire ecosystem. This information is a big data not only because of its large volume but also for its complex structure, diverse nature and time component.

Impact of Big Data on healthcare System

As this big data revolution is in its early days in the industry, most of the potential for value creation is still not tapped. The rapid changes in the analytical capabilities could shift the profit pools for care providers and reduce overall cost for management procedures in near future. This includes analytical capabilities to draw maximum insights from the complex data from the activities that are already occurring to develop models that have predictive potential that have not yet been used on scale.

Big data has potential make large positive impact on entire healthcare system; although there are many key players such as patients, healthcare professionals, hospitals, pharma companies, research organizations and it is very complicated ecosystem.

Key areas -

Care delivery It is utmost important to provide appropriate and timely treatment to patients. When there are heavy protocols in almost all disease management procedures, co-ordinated approach becomes very important to provide appropriate and timely care. All healthcare settings and care providers should have same information and they should direct their resources towards same objective, is an imperative. The focus should be on to avoiding duplication of effort in order to save resources and suboptimal management procedures.

Care providers Patients should always receive quality care from high preforming healthcare professionals that are well suited to their health problem to get best results. Role of care provider is vital in this ecosystem. For instance, knowledge of tasks performed by certain specialists and among those specialists those who have best proven outcomes.

Care value The value to delivered healthcare professional and patients should always be enhanced while maintaining or improving quality of care. This can be done using big data. For instance, healthcare professionals would be paid depending on the outcome of the patients or eliminating any abuse in the ecosystem such as fraudulent practices or waste of resources.

Innovation - Discovering new cost effective management procedures and therapeutic regimens is heavily dependent on accurate data. Innovation is continuous procedure involving all stakeholders of the ecosystem. Big data insights would be useful in enhancing effectivity of existing medicines and boosting Research and Developments outcomes. It is possible to find out high potential pharma molecules and targets using previously stored clinical trial information and applying proper analytical

tools. Pharmaceutical companies are already using dig data techniques now a days in improving clinical trials. In care delivery big data could be used in assessing effectivity of traditional patient management procedures.

As new more information becomes available it is possible to know what is the most effective and appropriate practice. For example, in care delivery it is possible to keep track on particular disease management procedures and compare different protocols for their effectivity. This can affect other stakeholders in the ecosystem and it is possible to manage that change with the help of big data efficiently.

Value Capture

Leaders in industry have already started using big data to enhance efficiency, effectivity and care delivery experience. For instance,

1. Care providers have started to implement the advanced systems that allows them to share patient information across all healthcare facilities and implement digital healthcare records practice in the organization. This has improved the efficiency of the entire ecosystem saving time and bringing down the cost of care delivery for the patients by managing resources efficiently.

2. AstraZeneca Pharmaceuticals have started a project using real time core data obtained from WellPoint and clinical trial data on colonic diseases that they have collected over years through different research projects and using insights drawn from that data to make decision on where to invest money on Research for development of drugs used for gastro-intestinal disorders. This will enable them to develop more effective drugs and will reduce time required to go through entire drug development process.

3. Blue Shield of California, along with Health Nant, have integrated a system that allows care providers, patients and planners to deliver evidence based healthcare using big data analysis. In this way they are managing to deliver more personalised care in a coordinated manner giving patients more satisfaction.

Potential of improvement in entire ecosystem using Analytics

For Patients -

Data based decision making system

1. To define the parameters that are important in creating value so that maximum efforts and resources could be directed in those areas to maximize patient satisfaction.

2. To analyse behaviour of stakeholders and how do they make decisions to capture maximum value and improve care delivery keeping focus on quality.

3. To evaluate value captured by patients developing assessment criteria and then providing recommendations to patients to get best outcomes.

4. To evaluate performance of providers developing assessment parameters and giving them suggestions to improve their practice so that they could fulfil their patients expectations.

5. To evaluate impact of personalised messaging targeted to address patient behaviour to prevent occurrence of particular diseases and to develop alternative effective outreach tool using big data.

6. To identify resource consuming processes and bottlenecks in the system and try to make them more efficient so that resources could be allocated to other processes. For instance, analysis of health claims requires large human power as well as it takes time.

Identification of practices that save the cost

1. To access different trends that are driving cost of healthcare. For instance, to identify patient category, disease condition, and healthcare provider where cost was much lower than otherwise expected. After identifying such trends the factors responsible for lower cost could be identified and implemented in other practice areas.

2. To evaluate cost differences between highest and lowest outcomes for similar processes at different places so that efficient, low cost practices with maximum outcome could be established.

3. To quantify matrices analysing factors driving best outcomes and providers characteristics and communicating them to care providers so that they could improve their practices and in a way outcomes.

For care providers

Improving patient management procedures focusing on quality care

1. Developing standard approach - to analyse processes which are effective i.e. better outcome with lower cost, and protocols to enhance quality care practices and patient satisfaction.

2. To find out future trends in particular practice area. For instance, expected number of new diabetic patients in a city could be calculated using trends in the contextual external data such as trend in carbonated drink consumption, trend in junk food consumption, trends in lifestyle changes etc. so that future risks could be stratified and resources could be allocated effectively to face the expected challenge.

3. To launch pilot programs and ensure their efficacy. For instance, vaccination program for polio in Africa could be planned by accessing real time information of number of polio patients in particular area.

4. To make investment decisions for clinical and operational projects. For instance, investment in certain healthcare facility after assessing number of different type of patients in that area in the past.

Ensuring consistent and quality data capture

1. To develop strategies to collect quality data using advanced medical devices and other smart devices such as smart-phones and websites so that more accurate insights could be drawn for future use.

2. To simplify processes of data collection by identifying barriers in collection processes and sharing that data to all appropriate stakeholders.

For manufacturers

Delivering maximum customer value

1. To analyse how their products affect cost of care for both patients and healthcare professionals to deliver maximize value.

2. To analyse performance of their products and its value in delivering care based on parameters such as ease of use, effectivity and efficiency and outcomes of disease management processes directly affecting patients satisfaction.

3. To make decisions based on data analysis about improvement areas in providing quality products to healthcare professionals and patients and to find out which characteristics of the product or service to be focused immediately.

4. To ensure that Research and Development takes into account the recent disease and care delivery trends as well as performance data of their existing products and services to channelize their funds to get desired outcomes.

Efficacy and safety products

1. To identify early safety risks in providing care using data from patients and health professionals to their products or services and analysing real time trends related to it.

2. To identify the factors that are challenging their products safety based on recent study and making decisions of which of those factors needs to be corrected immediately.

3. To monitor competition for the safety and efficacy of their products.

Collaboration decisions

1. To make decisions on collaboration in potential projects across all R & D communities and therapy research to broaden their resources.

Quantitative methods to be used

1. Time series and regression analysis could be used to find out trends and predicting possible future scenario. For example, certain disease trends associated with climate changes and other external factors.

2. Decision trees would be useful to make best decisions in uncertain situations. For example, a software could be developed using decision tree method to and using certain probabilities of outcomes of particular set of symptoms.

3. Linear programming would be useful to find outcome under large number of variables and how change in one variable could affect outcome. For instance, different cost driving factors could be analysed to define cost structure.

Barriers

1. Legislations related to privacy There are patient privacy concerns which are hindering the sharing the data. Also, the mind set of doctors is an issue. They feel it is unethical to share their patients sensitive data.

2. Collaboration among healthcare professionals and technology developers There were not combined efforts from doctors and technology developers until recent to address problems or to develop new sophisticated technologies.

Conclusion

Healthcare industry all across the world id facing challenges such as decreasing cost, improving outcomes and collaborating among different practice areas. To solve these problems healthcare facilities should start using big data analytics to manage clinical, financial and operational practices. The data could be used improve disease management, outcome management, reduce cost, innovate, to deliver quality care and to attain patient satisfaction.

Citations-

http://helicaltech.com/blogs/importance-of-business-intelligence-in-healthcare/

https://www.google.co.uk/search?q=importace+od+big+data+in+healthcare&ie=utf-8&oe=utf-

8&gws_rd=cr&ei=wE-mVdmVNoGAUeLVgYAL