Healthcare – Qsutra

How Data Science can help to Improve the present Healthcare Scenarios?

Umesh — Mon, 05 Oct 2020 13:03:41 +0000

According to the National Health profile 2018 released by Central Bureau of Health Intelligence, it was reported that the current doctor-population ratio is 1:11,082 in India. The current statistics is drastically lower than the ratio of 1:1000 as recommended by WHO.

Now, coming to the blog – we will discuss data science and “How data science can improve present healthcare sector in India?”

Let us first have a brief overview of Data Science – When we look to a few years back where traditional health information system (HIS) was largely applied. Patient information was recorded on sheets and everything was manual.

But now the scenario has changed, we moved to electronic medical records (EMR) and other advanced technology to improve the health care system. The term “Big Data” comes into play. We have probably heard about big data which is the latest buzz in the tech world. “What is big data?” Please try to think about it……

Let’s make it simple for us to understand, a big data is a large set of electronic health data (for e.g. EMR) which are complex and difficult to manage by using the traditional system.

We will come to our main agenda, “Data Science” – To get better insights or to unlock the real value from big data, we use data science for it. Data Science is an area of applying various techniques (data mining, machine learning, artificial intelligence, predictive analytics, etc.) to gain better insights about the current healthcare processes and predict future scenarios.

I would like to mention a few important areas where data science applications are valuable. First of all, healthcare is a vast sector which comprises of hospitals, pharmaceuticals, health insurance, diagnostics, clinical trials, telemedicine, equipment & supplies, etc. Data Science can be applied to various areas.

Prevent health problems by monitoring it
Improve accuracy in reports
Monitor clinical trials
To deal with epidemics and disease outbreaks
Predict the future consequences of a disease
Improve the quality of healthcare

Please think about other areas where we can apply data science?

Since we have a lack of manpower, we can use Artificial Intelligence and Machine Learning based medical solutions for the treatment in cities and remote areas as well. It can provide real-time patient information and assist with doctors for proper diagnosis and thus save time. By using data science, there will be fewer human errors and it will be speedy and accurate.

Organisations like Columbia Asia, Apollo, Manipal, Fortis Healthcare, etc. have been doing commendable work in the field of data science applications at healthcare with joint collaborations with tech giants like Microsoft, IBM, Google and so forth.

NB – Many Healthcare service providers are using Minitab to improve their healthcare processes. Minitab is a Data Analytics Software, where we can Predict, Visualize, Analyze and Harness the power of our data. With Recent Release, Minitab now Available on both Desktop and the Cloud.

Python Integration in Minitab. We can run Python scripts easily in Minitab and extend our analytical capabilities by collaborating with the Data Analyst, Data Engineer, Machine Learning Engineer and the Data Science Generalist.

We provide a wide range of Analytics & AI Solutions like Business Analytics, Digital Process Automation, Enterprise Information Management, Enterprise Decisions Management and Business Consulting Services for Organisations to enhance their decision support systems.

Why Biostatistics is Important in Healthcare and Pharmaceutical Industry?

Umesh — Thu, 06 Aug 2020 04:42:29 +0000

Have you ever come across the term “Biostatistics” before? I guess maximum will tell yes and minimum will tell no or vice versa. Relax, I will leave it to you.

Biostatistics is a branch of Statistics which deals with the data related to Medical Science, Public Health, Medical Research, Epidemiology and so on. In simpler words, when we apply Statistics in Biological Science – it is called Biostatistics.

Let’s discuss few scenarios like

A local PHC wants to know the proportion of people affected by an outbreak of certain disease and to find out the causes behind it and to prevent further circumstances.
During a clinical trial, there are many phases for drug or device development. And now the question is “How will they know whether the trial is going good or bad?”
In a pharmaceutical company, they want to know the dissolution rate of a particular tablet and to know whether it satisfies the desired rate or not. Similarly, they want to know the mean weight of a tablet to compare with other batches. Now, our question is “How will they do the analysis?”

In the above-mentioned scenarios, we apply Biostatistics concepts and do the analysis. Based on the analysis results, we draw inference about the given problem. So basically, Biostatistics is a method that comprises of a collection of data, summarizing, analyzing and drawing inferences.

By now, we have an overall outlook of Biostatistics Application in the Healthcare and Pharma Sector.

We will discuss few basic concepts of Biostatistics.

Data are the first thing which pops out on the head when we talk about any kind of analysis. Since we are dealing with data for analysis, we should know the basic concepts of it. Data are the actual pieces of information that we collect through our process or experiment. It can be categorized into categorical and numerical data. Categorical data are defined in categories or groups. For e.g. Male or Female, Yes or No, etc. Numerical data are further classified into Discrete and Continuous data. Discrete data are the data which take certain values. For e.g. No of patients, Devices, etc. Continuous data are the data which can take any values either fractional or decimal. For e.g. Blood pressure level, Height and Weight of the patient, etc.

Sampling is a method used in Statistical Analysis of a data where a specific number of samples are taken from a population for a study. For e.g. Health Ministry wants to know the opinion or suggestions of recently implemented health programme in Karnataka. Here the population – Karnataka and sample – Bangalore, Mysore, Mangalore, Hubli, etc.

Hypothesis testing is a method to determine whether the results are statistically significant or not. In simpler words, the process of drawing inferences (making decisions) about the sample with regards to the population as a whole is known as Hypothesis Testing.

Measurement System Analysis helps us to detect the amount of variation exists within a measurement system. Suppose during the initial clinical trial phase of a particular medicine, “If the data are not taken properly?” And move forward for further analysis. Then “What would be consequences of it?” Obviously, it may fail later on final phases.

Now, I would like to mention the final overview of Biostatistics Applications in Healthcare and Pharma Sector. It helps to a great extent during the clinical trials by designing the trial, sampling techniques, measurement system analysis and analyzing for appropriate results. We can improve our health policies, new drug policy, assessment of environmental protection guidelines, quality improvement in Hospitals and Pharmaceuticals, and so forth.

NB – Attend our Training Program, to know more about Statistics and Statistical Software. Our Training programs are divided into two sections – Statistical Training and Minitab Software Training. Statistical training starts with basic level to advanced level. Some of the Statistical training certified courses are Predictive Analytics Masterclass, Essential Statistics For Business Analytics, SPC Masterclass, DOE Masterclass, etc. Now coming to Minitab software training, starts with basic to advanced level. Some of the Minitab software training certified courses are Minitab Essentials, Statistical Tools for Pharmaceuticals, Statistical Quality Analysis & Factorial Designs, etc.

We also provide a wide range of Business Analytics Solutions and Business Consulting Services for Organisations to make data-driven decisions and thus enhance their decision support systems.

Is your Sample Size enough for doing the Analysis?

Umesh — Mon, 06 Jul 2020 05:39:46 +0000

I guess this is an important question we should ask ourselves before doing any kind of analysis. We should know the sample size required to do the analysis. Say, if we collect more samples than required – what will happen? We will waste our resources and time. Thus enhanced cost and complexity. On the other hand, if we collect fewer samples than our test may fail and hence less accuracy on results. On an extreme note, our test may fail to detect the important effect and could not meet the test objective. Hence we should determine the optimum sample size required for analysis.

Let’s discuss a very basic example to understand “why the determination of sample size is important in data analysis?” Generally, salt is a very important factor to determine the taste of food. We all know how much work is needed during the cooking process, right! Now, the hard work will be paid off if only we get a significant result i.e. food must be tasty. Say, if we put more salt than required or less salt than required – what will happen? We all know the consequence, right!

To avoid such circumstances in data analysis, “Concept of Power and sample size” comes into play.

What is a Power and Sample size?

It is a concept of estimating adequate sample size required for data analysis to achieve significant results. We already know sample size – it is a count of individuals or samples required for analysis. But you might be thinking about the term “power”. Power analysis tells us a number of sample size required to avoid any type of error in decision making. In other words, we can say that it is a probability of making the right decision irrespective of errors.

I would like to mention a few factors which affect the power analysis are sample size, variability and alpha value (level of significance). We should choose adequate samples so it will not hamper the power analysis. There could be a variation (spreadness of a data) in a process and thus results in a random error. We should use proper sampling procedure and measurement analysis to minimize variation in a process. Alpha value represents the probability of making the wrong decision when it is actually true. The common alpha values are 1% (0.01) and 5% (0.05).

In a ball bearing manufacturing industry, engineers are concerned that the bearing diameter has shifted away from the target of 0.6 cm. They consider a difference of 0.01 cm to warrant adjusting the equipment. From the historical data, the standard deviation is 0.005 cm.

Now the question is “How much data is needed for analysis?”

With reference from the power curve – for doing the analysis we need a sample size of 5 where alpha value is 0.05 and power is 0.9.

What are the common power values used?

When determining the sample size, we should put the optimum power value as to avoid any kind of error. Suppose in a test, if it has low power value than it might fail to detect an effect and if it has a higher power value than small effects might seem to be significant. Some of the common power values used are 0.8 and 0.9. Let’s say if your study has 90% power than it has a 90% chance of detecting an effect that exists.

There are various ways to determine power and sample size. The easiest and simplest way to determine it is by using the “Power and Sample size” option in MINITAB. With Recent Release, Minitab now Available on both Desktop and the Cloud.

NB – Want to learn, How to run Power & Sample Size Analysis in Minitab Software? Attend our Minitab Certified Training Program, starting from basic to advanced level. Some of the Minitab software training certified courses are Minitab Essentials, Statistical Tools for Pharmaceuticals, Statistical Quality Analysis & Factorial Designs, etc. Apart from Minitab training, we also conduct basic and advanced Statistical training. Some of the Statistical training certified courses are Predictive Analytics Masterclass, Essential Statistics For Business Analytics, SPC Masterclass, DOE Masterclass, etc.

Also we offer solutions to build a robust Enterprise Decisions Management system that can be very effective for organisational Decision Making.

Is your Data Reliable enough to do the Analysis?

Umesh — Mon, 09 Mar 2020 05:23:03 +0000

Data – What we need to do the analysis? But how much we are sure that the data we have collected for analysis is reliable enough! The main agenda of this post is to discuss data reliability, it’s importance and how to achieve it.

Let’s start with a brief example, suppose during a Clinical Trial “If the data are not taken properly?” And proceed further for analysis. Then “What will be the consequences of it?” Clearly, it may fail later and results in loss of lives, resources and time. Thus, the device measuring the host condition should produce reliable results.

Now, I would like to introduce the term called “Measurement System Analysis (MSA)” which helps to determine the reliability of a data for analysis. You must be curious to know about it, right!

Generally, MSA is a method to determine the measurement system in a process. The measurement system is a collection of measurement devices, measurement procedures and operators that are used to obtain a measurement. MSA helps us to detect the variation (i.e difference or disparity) exists in a measurement system. So we should evaluate our measurement system before conducting statistical process control, design of experiment, Predictive Analytics, or other statistical analysis. By doing so, we can be sure that our data are reliable for analysis.

We can apply MSA for two types of data namely attribute and continuous data.

Attribute data - When data collected are in the form of count or categorized in the form of group. For e.g. accept or reject, good or bad, and so on. In such scenario, we use Attribute agreement analysis.
Continuous data - When data collected are in the form of fractional or decimal value. For e.g. weight, length, time, temperature, etc. In such scenario, we use a Gage R & R analysis.

MSA is classified into Attribute agreement analysis and Gage R&R analysis based on data types used.

Example

Suppose in a Wheel Manufacturing Company, operators use gage to measure the diameter of the wheel. Are we 100% sure that the collected data is measured correctly or not?

With reference from the Gage Run Chart – Three operators measure 10 wheels, three times per wheel randomly. Here, we can examine the differences in measurements between operators. We can see that Operator B does not measure consistently, and Operator C usually measures lower than the other operators.

Let’s discuss a few terminologies used in measurement system analysis.

Accuracy – It is the difference between the measurement of a part and its actual value.
Bias – It is the measurement difference between the observed value and true value.
Linearity – It is the difference or a change in bias value w.r.t its size.
Stability – It is a measurement of how well the system performs over time or we can say, it is a change in bias value over time.
Precision – It is the closeness between the measured value and the true value.
Repeatability – It is a variation which occurs when the same operator measures the same part with the same device.
Reproducibility – It is a variation which occurs when the different operators measure the same part with the same device.

Why MSA is important?

It is a prerequisite for successfully implementing Lean Manufacturing, Six Sigma and Lean Six Sigma projects. It is also useful in the implementation of SPC, DOE, Enterprise Prediction Management, Enterprise Risk Management, Enterprise Risk Management, and Enterprise Quality Management processes. It gives the better reliability of the measurement system in a statistical way.

NB – Want to learn, How to run Measurement System Analysis in Minitab Software? Attend our Minitab Certified Training Program, starting from basic to advanced level. Some of the Minitab software training certified courses are Minitab Essentials, Statistical Tools for Pharmaceuticals, Statistical Quality Analysis & Factorial Designs, etc. Apart from Minitab training, we also conduct basic and advanced Statistical training. Some of the Statistical training certified courses are Predictive Analytics Masterclass, Essential Statistics For Business Analytics, SPC Masterclass, DOE Masterclass, etc.

What are the Applications of ANOVA in the field of Healthcare and Pharmaceutical Industry?

Umesh — Sun, 08 Mar 2020 06:08:29 +0000

Healthcare Industry is the largest sector in India which comprises of hospitals, pharmaceuticals, diagnostics, telemedicine, medical tourism and so on. We will discuss a few applications of ANOVA in the field of pharmaceutical and other health services. Before going to ANOVA, let us create some basic background and will be helpful to grasp in a wider way.

First of all, I would like to introduce the term called “Sampling”. Sampling plays an important role in healthcare data analysis, in fact in any kind of data analysis. It is a process of selecting a sample from a population for a specify study or analysis. For example, we want to conduct a study on a diabetes patient and here it is not possible to conduct a study on the whole population (say India) due to certain factors. So, what will we do? We will take a sample (say Karnataka, West Bengal, Rajasthan, Himachal Pradesh, Meghalaya) and do the study. By now, we must be clear about the concept of the sampling process.

Next, we will dig little on “Hypothesis testing” – based on a collected sample we need to draw inferences or to make a conclusion about the data, we need hypothesis testing. In simpler words, the process of drawing inferences (making decisions) about the sample with regards to the population as a whole is known as hypothesis testing.

Now, let’s discuss the basic sample tests viz. one sample t-test and two-sample t-test.

Suppose in a Pharmaceutical Company, QA officer collects a two sample groups (A and B) each of 10 tablets to measure the dissolution rate of a drug. We can compare which of these types have a better dissolution rate and hence we able to choose the best one. Here, we apply a two-sample t-test.

By now we know the basic concepts of sample tests, when we have one sample, we use one sample t-test and when we have two sample groups, we use two-sample t-test. Isn’t it simple?

In many real-life scenarios, we will have more than two sample groups say three or four sample groups then “What can we do?” In this case, ANOVA comes into play. Finally……right

ANOVA (known as Analysis of Variance) is a technique which is used to check whether the means of two or more sample groups are statistically different or not.

Suppose in the Healthcare Industry, we can use the ANOVA test to compare different medications and the effect on patients. If a company has 3 different medications for treating ulcers and by using ANOVA, we can determine the effectiveness of treating them. We can compare which medication works better for treatment and hence we able to choose the best one.

During the vendor quality evaluation, we can use ANOVA to evaluate the quality of product supplies received from different vendors. Similarly, we can apply in various healthcare scenarios.

Suppose in a Pharmaceutical Company, QA officer collects four sample groups (A, B, C & D) each of 6 tablets to measure the hardness of a tablet.

Since our p-value is 0.004 (From ANOVA Table) which is less than our alpha value 0.05, we can conclude that the means are significantly different from each other. Similarly, on an interval plot, we can see the mean differences between each type.

Let’s discuss a few of the assumption it holds.

NB – Want to learn the concepts of ANOVA and Application in Industries? Attend our Statistical Training Program starting from basic to advanced level. Some of the Statistical training certified courses are Predictive Analytics Masterclass, Essential Statistics For Business Analytics, SPC Masterclass, DOE Masterclass, etc. Apart from Statistical training, we also conduct Minitab Certified Training Program, starting from basic to advanced level. Some of the Minitab software training certified courses are Minitab Essentials, Statistical Tools for Pharmaceuticals, Statistical Quality Analysis & Factorial Designs, etc.

What is Healthcare Analytics? How can it help in Transforming Healthcare Services?

Umesh — Wed, 08 Jan 2020 06:31:07 +0000

As we all know that the Healthcare Sector plays an important role in our lives. Healthcare is a vast area which comprises of hospitals, pharmaceuticals, health insurance, clinical trials, etc. Hereby using analytics, we can improve in many ways. Although we have a shortage of manpower as we can see the differences between rural and urban areas. We are also lacking the advancement of technology and analytics. To overcome it, healthcare analytics helps to a great extent.

You might be thinking “What is Healthcare Analytics?”, don’t worry I am coming there. Since we are moving from the traditional health system to the modern health system where usage of electronic health records (EHR), diagnostic results, Health Management Information System (HMIS), etc. are at large. These activities produce a large set of data which is known as Big Data which are complex and difficult to manage. To get insights about the healthcare data, we apply analytics on it for a better outlook. Healthcare Analytics is a set of applying statistical models and computational logic to unlock real value from healthcare data. In a simpler way, it is a part of Data Science.

Classification of Healthcare Analytics

By now, you might have got a basic knowledge of healthcare analytics. Further moving on, healthcare analytics can be classified into –

Here, it uses real-time data to make a decision and get insights about the treatment. It is also known as Clinical decision support (CDS). It improves the cost and quality of healthcare.

It deals with financial stuff and gives the outlook of financial data. It helps in monitoring cash flows, revenue and expenses in an organization. Finance plays an important role in increasing value in an organization and gives the current profit scenarios. It also helps to predict the future profit scenario.

Here, we can optimize operational performance by using data insights to manage costs, patient claims, etc. In simpler words, we can say that it deals to improve the existing process.

Now, I would like to discuss a few data science disciplines which are widely used in healthcare analytics to get better insights from a data.

The process of extracting useful information from large sets of raw data (complex data) is known as Data Mining. Here, we use descriptive and inferential statistics for analyzing the extracted data. It holds great potential in healthcare to improve services and reduce costs.

Predictive Analytics is a method of applying statistical techniques combined with applied mathematics and computational science to predict and improve decision making strategy in given scenarios. Here, we can predict the outcomes of certain disease and prefer handy for its circumstances.

Machine learning is the trending buzz around the globe for its potentiality. Arthur Samuel coined the term “Machine Learning” and defined as “Field of study that gives computers the ability to learn without being explicitly programmed.” It is helpful for disease identification, diagnosis, robotic surgery, drug discovery, etc.

Artificial Intelligence is a field of computer science where machine or computer programs perform tasks normally which requires human intelligence. It is a study of how the human brain thinks, learn, decide and work when it tries to solve the problem. It helps in healthcare system analysis, consultation, precision treatment and so forth.

Some of the tech giants like IBM, Microsoft, Google, etc. are working with healthcare providers in India to improve the field of healthcare analytics and doing exceptional on it.