DIGITIZATION OF HEALTHCARE

INDUSTRY

MARKET RESEARCH REPORT

DEPARTMENT OF MANAGEMENT STUDIES

IIT ROORKEE

ii

TEAM MEMBERS

Jatin Dhawan

15810026

Harsh Bhushan

15810024

Aayush Rastogi

15810001

Ankush Partap Singh

15810005

Baljeet Singh

15810012

UNDER GUIDANCE

Dr. Jogendra Kumar Nayak

Assistant Professor, IIT Roorkee

iii

he authors of this report are highly grateful to Dr. J.K. Nayak, Assistant

Professor, Department of Management Studies, IIT Roorkee for providing this

opportunity to conduct a market research. The constant guidance and

encouragement received from him has been of great help for carrying out this

research work. Without his wise counsel, it would have been impossible to

complete the research work in this manner.

We would also like to express a deep sense of gratitude and thanks to all our

colleagues from IIT Roorkee who took out their precious time to respond to the

questionnaire which was designed to carry out this research work.

Finally, we are indebted to all the respondents of our survey from all over India

who contributed to our work.

Aayush Rastogi

Ankush Partap Singh

Baljeet Singh

Harsh Bhushan

Jatin Dhawan

T

ACKNOWLEDGEMENT

iv

his research aims at understanding the awareness about the online healthcare

services among the people of India. The research was conducted taking the

sample of population from various different states of India.

The research has demonstrated that most of the people in India are not aware

about the online healthcare consulting services. This indicates that there is a

communication gap between the consumers and the service providers. Those

who are aware of these services, most of them have never availed these services

online. Only a very small proportion of people have availed these services and

most of those belong to tier 1 cities like New Delhi, Mumbai or Pune. Also mostly

young generation was familiar with this sector while the elderly people were

unaware.

An interesting outcome of the research was that around 90% of the people who

have never availed these services would like to use such services in the future.

This indicates that there is a bright future for the Digital Healthcare sector in

India.

The research also indicated that non readiness of people to adopt such platform is

one of the reasons for low usage of these services and the biggest de-

motivational factor for people to use these services is the fear of false

recommendation and reviews. The study revealed that there is a perception

among the people that these online platforms are meant for the advertisement of

the doctors.

The major customer preferences for using the online healthcare services came

out to be; Online appointment booking, searching a nearby service/doctor,

storing health records and speedy resolution of queries. So, the overall study

indicates that there is a lot of scope in the Online Healthcare Industry if these

services are provided as per the customer preferences and customer satisfaction.

T

EXECUTIVE SUMMARY

v

Title Page No.

Team Details ii

Acknowledgement iii

Executive Summary iv

INTRODUCTION 1

LITERATURE REVIEW 2-3

RESEARCH PROCESS

I. RESEARCH OBJECTIVE 4

II. RESEARCH METHOD 5-8

FINDINGS

I. RESULTS 9-13

II. ANALYSIS OF RESULTS 14-20

CONCLUSION 21

TABLE OF CONTENTS

1

ealthcare is one of the most important industry in India. It has become one of the

largest sector, both in terms of revenue as well as employment. According to IEBF,

the Indian healthcare market is worth US $100 billion and is expected to grow to US $280

billion by the year 2020. With the emergence of the Digital technology as well as the

government initiatives such as Digital India, the Healthcare industry is paving its way

towards digitization.

The main purpose of digitization of Healthcare Industry is to provide one stop solution

for all the customers as well as to reach to the remote areas within India where a huge

potential for this industry could be exploited. Some of the services which are being

provided by some of the players in this Industry are; online appointment booking,

searching a nearby doctor, online storage of health records, price comparison for nearby

services, health related queries, prediction of check-up need with proper analytics

report, online payment gateway, video chat with industry specialist, etc.

In India, many of the companies have entered the digital healthcare segment and also

many new start-ups are coming up with innovating solutions in this segment. Some of

the popular companies offering online healthcare services are; Lybrate, Practo, Doc

Engage, Healthiply, Liv Health, Praxify, etc.

H

INTRODUCTION

2

here are three levels of health delivery systems in India which are categorized as

Primary level, Secondary level and Tertiary level. All these level are having either

Public system or Private system.

We have categorized the Primary level, Secondary Level and Tertiary level as below.

Level Public Systems Private Systems Primary Level Public health centres and

its sub-centres come under this section.

Private doctors or practitioners come under this section.

Secondary Level Public Hospitals in various districts are the part of this level.

Private nursing homes and hospitals being operated by private bodies.

Tertiary Level Government Medical colleges and Institutions.

Corporate hospitals and Private medical colleges.

Government of India have taken many initiatives in recent years for digitalization of

healthcare sector and going with the programs like e-Health etc. Many companies like

TCS, Wipro etc. are providing support to government in establishing these projects in

country.

Electronic Medical record Hospital automation and telemedicine is coming into picture

and making an impact in healthcare industry of India. Apart from these many private

players are also coming with Digital health care solutions for the society. Center for

Development of Advanced Computing (CDAC) is working for the automation in large

hospitals this will also bring a change in perception of people about digital health care

services being provided by some private players in our country.

T

LITERATURE REVIEW

3

In electronic medical record system database of medical history of any individual will be

kept safe and secure for generations which will help individuals as well as government

for analysing and monitory status of health in the country. Private players will also be

benefitted by it as they will also get some edge for them in this system. People will get

idea about these kinds of services by government campaign and these private players

can provide some high quality services and turn those customers into their direction.

Many hospitals have also implemented these kinds of technologies into their

departments. Hospital SGPGIMS, Lucknow have implemented Tele-health care and

Distant Education in Medicine and Tele-mentoring into their system. Amrita Institute of

Medical Sciences, Kochi has 36 telemedicine centres all over India.

All these kinds of initiatives in health care sector in country will give a boom to Digital

health care services in India; all these kinds of initiatives give a right indication about the

bright future of Online Health care consulting market in India.

Department of Information technology is going with R & D projects. Center for

Development of Advance Computing is working on a telemedicine software and Media

lab Asia Initiatives is working in collaboration with many hospitals.

4

Research Objective

“Perception of Digitalization in Healthcare Industry”

Objective:-The objective of this marketing research is to assess the perception of

people towards digitalization of healthcare industry and to predict its future.

There are many online health care services in market these days so we need to analyse

the market that how much penetration is in the market by these kinds of services on the

basis of the data we have to predict about the future of these services whether these

services will work in future or they need to have some modifications.

What is the perception of customers those who have availed these services and

the features liked or disliked by these users?

What is perception of the customer who never availed but they are aware about

these kinds of services and if they would use these services than what should be

their expectations from these kinds of services?

What are the demographics of user using or want to use these services in the

future?

Which kind of feature people would like to have in these kind of online healthcare

services?

RESEARCH PROCESS

5

Research Method

Research Approach

Our main goal was to target a specific number of people within the boundaries of

country; we targeted Indian citizens with specific number of relevant questions related

to our study. We selected questions on the basis of study to be carried out so that we get

the right findings from the data. We believed that questions are specific to our study and

can bring out real perception of individuals towards the services.

To speed up the process of collecting data we employed an online survey which very

much common these days. Google forms are used for surveys which are reliable and free

source for collecting data.

To speed up the process for collection of data we used the social networks, emails etc. to

deliver the survey form. Research team analysed data using various tools and methods

for accurate results.

Data Collection

We have taken the help of Google forms to collect the data. This platform was used as it

is speedy, accurate and has no cost involved to collect data. Data is collected in excel

format in this platform which gives the advantage while make analysis of data.

We divided the geographies and circulated the form to those which have potential and

future customers for our study.

Data collection is the most tedious task in any marketing research project; it takes long

time for the collection of data. Since most of the people are not willing to give responses

and many gives the wrong responses, so it’s very important that survey form should be

sent to right kind of people.

6

Questionnaire

We designed the questionnaire which contains two parts to get the data from the

desired individuals. First part contained the questions related to demography and about

the awareness of digital healthcare services.

If person is not aware about these services than further questions were not asked from

that person.

Scales used in questionnaire was the LIKERT, nominal scales.

Q-1:- Are you aware of online Health care consulting services?

O Yes O No

Q-2:- Have you ever availed any health Care service online?

O Yes O No

Q-3:- Will you use it in future?

O Yes O No

Q-4:- How was your experience of accessing the online service?

1 Highly Satisfactory

2 Satisfactory

3 Neutral

4 Dissatisfactory

5 Highly Dissatisfactory

Q-5:- Which online platform do you prefer the most? (Choose only one)

Lybrate

Practo

Doc Engage

Healthiply

Liv Health

Plaxify

Other _______________________________

7

Q-6:- Was there any difference between the service which you availed online and that of

offline?

Yes

No

Can’t Say

Q-7:- Rate on a scale of importance, the availability of following services from an online

healthcare service platform? (Scale of 1 to 5)

Searching a nearby service

Storing your Health Records

Avail discount and monetary benefits

General suggestion on common diseases

Comparison of prices of available health services nearby

Speedy resolution of queries

Ratings or feedback of services

Regular notification or reminders of booking, important information, etc.

Calculation of expenditure relating to a particular health condition.

Prediction of checkup need with proper analytics report.

Online payment gateway

Video chats with industry specialists

Networking with people having same issues or interests

Healthcare related jobs

Q-8:- How important to you are the following aspects in deciding which online healthcare

service platform to use? (Scale of 1 to 5)

o End User interface

o Number of Doctors listed

o Most Economical

o Provision of other complimentary services like Health Insurance, Tele-Medicine etc.

8

Q-9:- Rate on the degree of likeliness, how each of the factors may de-motivate users to

use online healthcare service platform? (Scale of 1 to 5)

Perception of Inflated Rates

Perceived biasness for some Doctors, Products etc.

False Recommendations and Reviews

Less Information Available Online

Fear of other hidden charges

Non Readiness of users to use such platform

Q.10:- How do you think can online healthcare service platform can penetrate into rural

or semi urban parts of India? (Scale of 1 to 5)

Integrating with Government Healthcare benefits

Setting periodical Health checkup camps

Offering provision of transportation like Ambulances, PVT Transport etc.

Dedicated personal assistance to rural users throughout the transaction.

Offering economical services like non specialist doctors, Registered Medical

Practitioners etc.

Collaborating with NGO’s and volunteer groups

Providing insurance covers

Scale Defined- 1= Not at all important

2= Slightly Important

3= Important

4= Fairly Important

5= Very Important

Analysis Overview

To accomplish we used factor analysis and correlation we want to analyse which all

factors are affecting consumer in same way. We also want to observe how factors are

affecting them to use services in future.

9

RESULTS

The survey results are shown below.

We got total responses 173 responses.

Fig-1 Awareness of people about the healthcare services

FINDINGS

10

Out of 173 respondents only 73 were aware about these online healthcare services,

further data is about the people who were aware.

Fig-3 Respondents who availed online health care services.

Fig-4 Willingness to use services in future

Fig-5 Experience of accessing the online service

11

Fig-7 Preferred online services

Fig-8 Difference between the service availed online and that of offline

12

Fig-9 Preferences for availability of services from an online healthcare service platform

13

Fig-10 Aspects in deciding which online healthcare service platform to use

Fig-11 Factors that may de-motivate to use online healthcare service platform

14

ANALYSIS OF RESULTS

For purpose of analysis we used factor analysis and correlation between variables.

Important findings of same are tabulated below.

Correlation matrix

Above are findings of correlation among the different variables that are used to determine the

perception of consumers about online healthcare services.

After finding correlation we found KMO and Cronbach’s Alpha as below.

Eigen values for factors and respective variability and cumulative %.

Correlation matrix (Pearson (n)):

Variables

Online

Appoint

ment

Nearby

Service

Storing

Health

Records

Discou

nt

General

Suggesti

ons

Price

Comparis

on

Speedy

Resolutio

n

Feed

back

Reminde

rs

Expendit

ure

Calculati

on

Predictio

n of

Checkup

Online

Payment

Gateway

Video

Chats

Networki

ng with

Patients

Healthcare

Jobs

Online Appointment 1 0.555 0.386 0.249 0.247 0.288 0.363 0.372 0.292 0.327 0.224 0.332 0.165 0.139 0.122

Nearby Service 0.555 1 0.551 0.397 0.568 0.575 0.560 0.520 0.284 0.451 0.410 0.471 0.309 0.275 0.255

Storing Health Records 0.386 0.551 1 0.453 0.605 0.589 0.637 0.623 0.540 0.534 0.662 0.318 0.324 0.338 0.209

Discount 0.249 0.397 0.453 1 0.403 0.613 0.404 0.409 0.575 0.525 0.532 0.394 0.368 0.396 0.383

General Suggestions 0.247 0.568 0.605 0.403 1 0.634 0.630 0.509 0.407 0.566 0.572 0.400 0.514 0.435 0.273

Price Comparison 0.288 0.575 0.589 0.613 0.634 1 0.594 0.570 0.595 0.578 0.592 0.499 0.631 0.492 0.327

Speedy Resolution 0.363 0.560 0.637 0.404 0.630 0.594 1 0.543 0.506 0.524 0.594 0.518 0.500 0.287 0.250

Feedback 0.372 0.520 0.623 0.409 0.509 0.570 0.543 1 0.529 0.594 0.568 0.477 0.542 0.457 0.372

Reminders 0.292 0.284 0.540 0.575 0.407 0.595 0.506 0.529 1 0.658 0.579 0.412 0.561 0.516 0.493

Expenditure Calculation 0.327 0.451 0.534 0.525 0.566 0.578 0.524 0.594 0.658 1 0.800 0.685 0.504 0.429 0.429

Prediction of Checkup 0.224 0.410 0.662 0.532 0.572 0.592 0.594 0.568 0.579 0.800 1 0.579 0.500 0.323 0.376

Online Payment Gateway 0.332 0.471 0.318 0.394 0.400 0.499 0.518 0.477 0.412 0.685 0.579 1 0.534 0.312 0.345

Video Chats 0.165 0.309 0.324 0.368 0.514 0.631 0.500 0.542 0.561 0.504 0.500 0.534 1 0.586 0.454

Networking with Patients 0.139 0.275 0.338 0.396 0.435 0.492 0.287 0.457 0.516 0.429 0.323 0.312 0.586 1 0.626

Healthcare Jobs 0.122 0.255 0.209 0.383 0.273 0.327 0.250 0.372 0.493 0.429 0.376 0.345 0.454 0.626 1

Eigenvalues:

F1 F2 F3 F4 F5 F6 F7 F8 F9

Eigenvalue 7.191 1.092 0.621 0.384 0.308 0.187 0.158 0.029 0.004

Variability (%) 47.941 7.277 4.143 2.561 2.055 1.245 1.051 0.193 0.027

Cumulative % 47.941 55.219 59.361 61.922 63.977 65.222 66.273 66.466 66.493

KMO 0.867

Cronbach's alpha: 0.927

15

With the help of tables containing Eigen values we developed scree plot for the factors

Fig-Scree plot for the factors and respective Eigen values

Technique we used for rotation of factors is Varimax rotation results of which are

tabulated below

Factor pattern after Varimax rotation:

D1 D2 D3

Online Appointment 0.134 0.056 0.537

Nearby Service 0.158 0.149 0.856

Storing Health Records 0.486 0.157 0.580

Discount 0.437 0.365 0.295

General Suggestions 0.403 0.300 0.533

Price Comparison 0.451 0.433 0.509

Speedy Resolution 0.479 0.191 0.578

Feedback 0.424 0.374 0.487

Reminders 0.540 0.512 0.217

Expenditure Calculation 0.741 0.340 0.296

Prediction of Checkup 0.860 0.204 0.271

Online Payment Gateway 0.497 0.282 0.337

Video Chats 0.383 0.597 0.235

Networking with Patients 0.111 0.863 0.164

Healthcare Jobs 0.226 0.648 0.065

Values in bold correspond for each variable to the factor for which the squared cosine is the largest

16

Online Appointment

Nearby Service Storing Health Records

Discount

General Suggestions

Price Comparison

Speedy Resolution

Feedback

Reminders

Expenditure Calculation

Prediction of Checkup

Online Payment Gateway

Video Chats

Networking with Patients

Healthcare Jobs

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

D2

(1

7.7

1 %

)

D1 (21.88 %)

Factor loadings (axes D1 and D2: 39.59 %) after Varimax rotation

17

Values in bold correspond for each observation to the

factor for which the squared cosine is the largest.

41 0.250 0.071 1.177

42 -1.895 -0.768 1.644

43 0.476 0.032 0.610

44 0.015 1.408 1.015

45 0.776 -1.538 1.117

46 1.387 1.544 -1.091

47 1.296 -0.352 0.067

48 1.353 -1.673 0.196

49 -1.096 -0.518 -0.558

50 -0.617 -0.507 0.610

51 0.620 0.082 0.103

52 0.573 -0.284 0.464

53 0.402 0.216 -0.268

54 -0.597 -0.026 0.034

55 0.874 -0.095 -1.614

56 -0.487 0.689 -0.258

57 -0.348 0.347 0.910

58 -1.817 0.109 0.896

59 -0.584 0.908 1.125

60 0.164 1.116 0.693

61 0.429 -0.081 0.391

62 0.815 -1.372 0.168

63 1.330 -0.425 0.067

64 -0.599 1.020 0.172

65 0.483 0.828 0.345

66 -1.096 0.286 0.348

67 0.312 -0.141 -0.107

68 1.904 -1.849 0.150

69 -0.473 1.394 0.059

70 0.774 -0.848 0.631

71 -0.528 -0.029 -1.296

72 -0.096 1.204 0.722

73 -0.506 -0.783 -0.197

Factor scores after Varimax rotation:

D1 D2 D3

1 0.075 -0.343 0.719

2 0.647 0.906 -0.818

3 -0.745 -1.363 -0.526

4 0.013 -0.163 -0.339

5 0.449 0.025 -0.656

6 -0.451 -0.171 1.109

7 -1.260 -0.732 0.914

8 0.296 0.716 -0.146

9 1.515 -1.334 0.341

10 -1.224 0.263 1.269

11 0.892 1.011 0.732

12 0.726 -0.796 0.718

13 -0.943 -0.154 -1.596

14 -0.354 0.042 -0.931

15 0.406 0.808 0.922

16 -1.119 0.400 -1.694

17 1.159 0.509 0.690

18 0.945 1.390 0.640

19 -0.578 0.859 -1.056

20 -0.350 0.346 0.526

21 1.035 -2.145 -1.044

22 1.385 0.780 -0.158

23 -0.234 -0.535 -1.842

24 -1.466 -1.098 -2.264

25 0.875 1.523 0.422

26 -1.517 -0.062 -1.029

27 -0.019 -1.279 1.501

28 -1.309 -1.023 -0.657

29 -0.736 -0.198 1.353

30 1.484 1.506 -0.964

31 0.553 -0.914 -0.173

32 0.497 0.384 -0.742

33 -1.652 -1.307 -2.501

34 -0.859 1.098 -0.669

35 -0.780 0.653 0.048

36 -1.226 -0.760 0.441

37 -0.354 0.042 -0.931

38 0.340 0.820 -0.348

39 1.184 1.356 -0.142

40 -0.798 -1.020 0.552

18

Findings from factor analysis

From all the different variables, for the desirability of a specific feature in an online healthcare

service the users could be classified into three major factors.

Factor 1- Money wise

o Desirability for- Discount Timely reminders Expenditure calculations (budgeted) Check-up with proper analysis report Online payment gateway.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16 17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49 50

51

52

53

54 55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3

D2

(1

7.7

1 %

)

D1 (21.88 %)

Observations (axes D1 and D2: 39.59 %) after Varimax rotation

19

Factor 2- The socialist

o Desirability for- Video chat Option for networking with patients. Seeking healthcare jobs

Factor 3- The convenience seekers

o Desirability for- Online appointment Nearby service Health records on the cloud General suggestions Price comparisons Speedy resolution feedback

Findings from P-Value based test

Before this study we assumed that people who had used this service will surely use in future, to test this assumption we conducted a P-value based test with the same assumptions.

Test Summary

Summary statistics:

Variable Observations Obs. with missing data Obs. without missing data Minimum Maximum Mean Std. deviation

Used or Not? 72 0 72 0.000 1.000 0.361 0.484

Will Use it or Not? 72 0 72 0.000 1.000 0.917 0.278

Correlation matrix (Pearson):

Variables Used or Not? Will Use it or Not?

Used or Not? 1 0.122

Will Use it or Not? 0.122 1

Values in bold are different from 0 with a significance level alpha=0.1

20

P-Values for the test

As P-Value is greater than our significance level (0.307>0.1) we reject our hypothesis hence we can’t say that people who had used this service will surely use in future.

By factor analysis and P-test we had findings about the online healthcare services that will be giving us great edge in concluding about the research.

p-values:

Variables Used or Not? Will Use it or Not?

Used or Not? 0 0.307

Will Use it or Not? 0.307 0

Values in bold are different from 0 with a significance level alpha=0.1

21

rom all the users the most sought after service from an online health care

service platform was found to be locating a nearby doctor, so the companies

in this field shall focus on providing these kinds of nearby doctor locating

services to capture the more market share.

In deciding amongst which online health care platform service to be used,

according to our research most people tend to go for the most economical

service.

As findings also suggest that there is huge lack of awareness regarding these kinds

of services in market, so companies are recommended to make people aware

about these online services by better campaigns.

There is good market share for these services in future if they are channelized and

implemented in more customer friendly manner.

For setting up online health care services in rural or semi urban part of India we will the

companies for setting periodical health check-up and providing transportation facilities

like ambulances.

F

CONCLUSION