application of classification technique and … journal of engineering technology, management and...

International Journal of Engineering Technology, Management and Applied Sciences

www.ijetmas.com May 2015, Volume 3 Special Issue, ISSN 2349-4476

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1 Selvan C,

2 Raju R,

3 Jitendranath Palem,

4 Marie-Claude Guérin

Application of Classification Technique and Sentiment

Analysis Based Text Analytics to the Patient Feedback

Management System

1 Selvan C,

2 Raju R,



1 Corresponding Author: Selvan C, Department of Industrial Engineering,

Anna University, Guindy, Chennai, 2

Raju R, Professor, Department of Industrial Engineering, Anna University, Guindy, Chennai,India 3 Jitendranath Palem, Quality Subject Matter Expert - Program Manager, IBM India

4 Marie-Claude Guerin, Master Linguist Engineer, SPSS Text Analytics, IBM France

ABSTRACT

This case study presents the application of Sentiment Analysis based Text analytics applied for the

Patient feedback management System at multi super speciality hospital in India. This technique was

successfully implemented in order to improve the quality of care and patient‟s satisfaction levels by

mining the patients‟ opinion and feedback data using text analytics and identifying the specific

problematic departments which were the major source of patient dissatisfaction. This study breaks

the myth of the hospital management team who were under assumption that “all the patients are

satisfied at every stage as everyone getting the equal quality of treatment”. This study proves that

patient‟s satisfaction level varies at every stage of their treatment life cycle and also it differs among

patient types and varies at every stage of treatment. By leveraging the Text analytics the hospital top

management derived the optimum countermeasures which resulted in improving patient satisfaction

across all departments by reducing complaints and improving patient care.

Keywords Text Analytics, Text Mining, Opinion Mining, Sentiment Analysis, Decision Tree, Patient

satisfaction

1. INTRODUCTION

Treating patients as customers has an adverse effect in the healthcare industry as well as on society.

Customer retention is one of the top priorities for every organization however, as the multi super

specialty hospitals adopted the corporate level service strategy and started providing the superior

facilities to all the patients, every single patient visiting their hospital are treated with high respect

and superior service, while this is true for the majority of hospitals, this case study reveals the

consequences of the corporate style of service strategy and the potential risks that would severely

affect the hospital revenue in the future if they do not adapt the analytical approach to digest the

unstructured data collected from the various feedback systems across all the departments and

branches in all locations. It is important to note that, treating patients as customers would completely

defeat the purpose of hospital industry and the medical profession itself in the long run. Customer

retention strategies are at the core for all type of industries, except hospital industry. Since patients

are not customers and hospitals never expect patients to visit their hospital for the same diseases

repeatedly despite of long medication courses. In this paper, we described how we have saved one

multi super specialist hospital which was about to experience a crisis situation which would have

affected the financial aspects as well as reputation and brand. This study uses statistical techniques



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and performed text analytics to improve customer satisfaction, moreover this can lead to new

ventures similar to “Patientslikeme”social web platforms in India as well. The emphasis is on the

importance of establishing a culture of clinical service excellence by reducing patients‟ complaints

which improves the patient experience and satisfaction which intern improves profitability. This has

been the significant reason for the paradigm shift in the delivery of care in many hospitals

worldwide. Creation of a competitive advantage in such hospitals is mainly based on that world class

service offered by them to their patients. One of the main reasons why many of the organizations fail

in delivering service excellence is that, providing service excellence involves the combination of

many key factors such as design of service processes, organizational culture, staff members

participation and performance of the organization which leads to not caring enough the soft side of

the service which is patient satisfaction and perception. A complimentary benefit can be obtained

when clinical excellence is combined with service excellence. Such an increased focus on service

delivery can greatly impact the success of hospitals. The following are objectives of this study:

Identify the key touch points that affect the patient satisfaction at hospital

Assess how these key patient touch points are rated by the patients in “patient feedback

management system”

Determine the effect of reducing patient complaints and measure the overall satisfaction.

1.1 Data Design and feedback collection method

Patient feedback management system is a web based portal being used centrally across all branches

by the multi super speciality hospital. The data is collected for all the 16 branches of the hospital.

Total 2347 patient‟s responses were captured and used for the analysis. We assessed the touch points

at various stages of patient treatment through the survey and performed text analytics to identify

hidden problems in order to improve the patient satisfaction level.

1.2 Solution methodology

This study used the decision tree classification method (C5.0 Algorithm) due to the nature of its

speed, memory usage and its support for boosting, weighting, winnowing for identifying the focus

area of opportunities and order of priority to focus on corrective actions. Traditionally, most of the

data analysts uses pareto analysis to identify the vital few factors or the top focus areas, since this

conventional approach does not focus on soft elements such as satisfaction levels and actual feelings

of patients and the impact on service. Moreover, the categorization of feedback responses provided

in lengthy text box contains lot of description about their feelings, appreciations, concerns, issues,

challenges. Using Text Analytics, it helped us to understand the actual emotions or sentiments

contained in patient's feedback text. The unique aspect of this study lies in leveraging the decision

tree for identifying the problematic area of opportunities to implement the countermeasures in order

to improve patient satisfaction levels. This can be further used for predicting the complaints type in

specific departments.

1.3. Results:

Based on the text analytics recommendations, the hospital management staff understood the patients‟

perceptions and feelings. This resulted in reduction of 72% patient complaints across all top

contributing areas.

2. LITERATURE REVIEW

The thorough literature review study helped us to realize that there are no notable case studies in the

area of leveraging sentiment analysis based text analytics for the patient‟s feedback management



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system. “Patientslikeme” is a digital social network website where patients of chronic, life-changing

diseases share detailed quantifiable information about themselves, their diseases and their treatments

„effectiveness (Christopher S. Rollyson and associates 2008). Transformation of healthcare is

underway from sellers‟ market to consumers‟ market, where the satisfaction of the patients‟ need is a

primary concern while defining the service quality (Fellani Danasra et al. 2011). Healthcare

experiences of a patient can be measured from the patient complaint because a complaint might

suggest unsatisfactory or unsafe services. (Pichert et al. 2008) demonstrated an association between

unsolicited patient complaints and physicians‟ risk management profiles. They delineate about how a

responsive healthcare organization benefits by recording the patient complaint, systematically

analysing and aggregating it in order to improve service quality, safety and reduce lawsuit risks.

(Sage 2002) linking patient complaints and malpractice risks noted that such an association helped to

forge stronger links between the „customer satisfaction‟ side of the healthcare and the „clinical

safety‟ side.

Furthermore, (Hsieh et al. 2005) noted that many healthcare organizations hardly use patient

complaints to promote higher standards of care. In the recent years, several authors have suggested

several ways of promoting quality service and mitigating patient risks. (Gurses and Xiao 2006) found

that communications between healthcare teams and patients uncovered unmet needs and improved

clinical outcomes as well. Levinson and Gallagher (2007) emphasized the significance of disclosing

medical errors to patients. They suggested that physicians‟ error disclosures might create

opportunities for patients to help improve safety and quality. Coulter and Ellins (2007) have stressed

the effectiveness of strategies for informing, educating and involving patients.

For more than a decade, even though many healthcare organizations have supported

awareness

on feedback mechanism and many interventions to reduce the number of patient complaints thereby

increasing the patient satisfaction, a concrete optimal statistical model to assess the critical patient

touch points with a structured methodology within less amount of time was found missing. This

study shows the patients‟ complaint categories and in which area most of the complaints are

originating.

The scope of the text analytics in this case study is for the categories patients‟ "Complaints"

and "Suggestions" and for the decision tree model, all the three types (complaints, compliments,

suggestions) of feedback taken into consideration.

In the SPSS Modeler, The classification technique used the Area Name as the “input field”

and the “Comment type” as the target field. The Area name column was coded into nominal

variables as shown in Table 1.

Table 1 Area names and corresponding complaints and suggestions

Serial

number

Area name(issue area) Complaints

and

Suggestions

1 Administration 182

2 Nursing 106

3 House Keeping 93

4 Rooms 75

5 Front office 71

6 Billing 50



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7 In-room Dinning 45

8 Medical Services 38

9 PHC 28

10 IP Service 21

11 Investigation Services 17

12 Cardiology 13

13 Pharmacy 11

14 Public Areas 10

15 Operation Theatre 8

16 Restaurants 6

18 International Patient Services 3

19 Dialysis Unit 3

20 Telephones 2

21 Bio Medical 1

22 Laundry 1

23 Blood Bank 1

24 Ambulance service 1

2.1 METHODOLOGY

Assessing the various patient touch points at different stages of patient life cycle to reduce the

number of patient complaints in the hospital will satisfy the customers. (Internal and External)

Feedback collected from 2347 Patients and 577 complaints were reported

Metric - Number of Patient Complaints

Baseline - 577 Complaints per month / measured over year

Goal - 121 Complaints per month

2.2 Data Collection summary

Feedback responses from 2347 respondents extracted from patient feedback management system and

the data period of 1 year. The data contains the feedback concerned with frontline, in-patient, out-

patient, health check-up and post discharge. The descriptive summary of data shows that 18%

complaints (577) were recorded, 75% of compliments (2351) and 7% of suggestions were recorded

(216).

The classification technique used the Area Name as the input field and the Comment type as

the target field. The Area name column was coded into nominal variables

Variables recoded in IBM® SPSS® Statistics

Comment type column coded into three nominal variables such as

compliment = 1

complaint= 2

suggestion= 3

3. ANALYTICAL PHASE – MODEL BUILDING

Figure (1) represents the IBM® SPSS® Modeler Premium stream building to apply the decision tree

model (c5.0 algorithm) to identify the area codes which have a high complaint percentage that needs

to be targeted for improvement with minimal effort.



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Figure 1. SPSS Modeler Stream for creating decision Tree using the department names (area code)

and corresponding patients feedback data

3.1. Model summary output

area code in [1.000 8.000 15.000 19.000] [ Mode: Compliment ] => Compliment

area code in [ 2.000 3.000 5.000 7.000 9.000 10.000 11.000 12.000 14.000 17.000 18.000

20.000 21.000 22.000 23.000 ] [ Mode: Complaint ] => Complaint

area code in [ 4.000 6.000 13.000 ] [ Mode: Suggestion ] => Suggestion

area code in [ 16.000 ] [ Mode: Complaint ]

Survey Name in [ "Frontline Feedback" "Post Discharge" ] [ Mode: Complaint ] =>

Complaint

Survey Name in [ "In-Patient" ] [ Mode: Compliment ] => Compliment

Survey Name in [ "Out-Patient" "Personalized Health Check" ] [ Mode: Complaint ]

Complaint

Figure 2: Decision Tree output generated by IBM® SPSS® Modeler

3.1.1 Decision tree model output interpretation



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Referring to the C5.0 decision tree in Figure 2, the root node classification table calculated the

proportions of each comment type category. Complaint category feedback covers 18% of the total

feedback. There are 4 nodes generated from the root node. Each node contains the relative

proportions of comment type categories and corresponding area code the comment type belongs to.

The point of interest is to identify which area codes (department) need to be targeted in order to

reduce complaints with minimal efforts. Referring to Node 4, the complaint percentage is 50% and

the corresponding survey type groups.

Frontline feedback, Post discharge are dominant groups with maximum complaints belonging

to the area code 16, i.e. nursing department.

The second top contributing departments to focus on are mentioned in Node 3 corresponding

to the area codes 4, 6, 13, i.e. Blood bank, Dialysis unit, IP service. The blood bank related issues

were due to the IT infrastructure issues which were fixed by conducting another research program

and enclosed the complete case study in the thesis.

Table 2 presents the cross tabulation summary performed through hypothesis testing using

the test of association (Chi-square) to validate the hospital management team‟s assumption that the

feedback responses are evenly distributed across all patient types and no relationship between patient

type and their response.

Table 2: Feedback type versus the feedback type category cross tabulation

SurveyName * CommentType Crosstabulation

CommentType

Total Complaint Compliment Suggestion

SurveyName Frontline

Feedback

Count 13 0 0 13

Expected

Count 2.4 9.7 .9 13.0

% within

SurveyName 100.0% 0.0% 0.0% 100.0%

% within

CommentType 2.3% 0.0% 0.0% .4%

% of Total .4% 0.0% 0.0% .4%

In-Patient Count 99 244 117 460

Expected

Count 84.4 344.0 31.6 460.0

% within

SurveyName 21.5% 53.0% 25.4% 100.0%

% within

CommentType 17.2% 10.4% 54.2% 14.6%

% of Total 3.1% 7.8% 3.7% 14.6%

Out-Patient Count 13 5 6 24

Expected

Count 4.4 17.9 1.6 24.0

% within

SurveyName 54.2% 20.8% 25.0% 100.0%

% within

CommentType 2.3% .2% 2.8% .8%



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% of Total .4% .2% .2% .8%

Personalized

Health

Check

Count 9 46 20 75

Expected

Count 13.8 56.1 5.2 75.0

% within

SurveyName 12.0% 61.3% 26.7% 100.0%

% within

CommentType 1.6% 2.0% 9.3% 2.4%

% of Total .3% 1.5% .6% 2.4%

Post

Discharge

Count 443 2056 73 2572

Expected

Count 472.0 1923.3 176.7 2572.0

% within

SurveyName 17.2% 79.9% 2.8% 100.0%

% within

CommentType 76.8% 87.5% 33.8% 81.8%

% of Total 14.1% 65.4% 2.3% 81.8%

Total Count 577 2351 216 3144

Expected

Count 577.0 2351.0 216.0 3144.0

% within

SurveyName 18.4% 74.8% 6.9% 100.0%

% within

CommentType 100.0% 100.0% 100.0% 100.0%

% of Total 18.4% 74.8% 6.9% 100.0%

3.1.3. Hypothesis testing

H0: There is no significant relationship among patient type and their satisfaction levels

Ha: There is a significant relationship among patient type and their satisfaction levels

3.1.4. Cross tab summary interpretation

Based on the cross tab summary table shown in table 3, it is clear that there is a huge difference

between observed and expected values. The same is confirmed in the asymptotic significant level P

value in the chi-square table, which is less than 0.05 favouring the alternate hypothesis. Below

section contains the text analytics results obtained and the data insights. This proves that the patient‟s

feedback satisfaction level has strong association with patient type (In-patient, out-patient,

personalized health check-up, post-discharge)

Table 3. Chi- Square output – Decision tree classification

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 475.812a 8 .000

Likelihood Ratio 362.675 8 .000

N of Valid Cases 3144



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4. Sentiment analysis based Text analytics on the patient feedback data

Table 4 shows the output generated by IBM® SPSS® Modeler Text Analytics for the patients‟

feedback automatically categorized into negative, negative functioning, negative budget, negative

attitude, negative feeling, and negative competence. Category building refers to the generation of

category definitions and classification through the use of one or more built-in techniques, and

categorization refers to the scoring, or labelling process whereby unique identifiers (name/ID/value)

are assigned to the category definitions for each record or document. Table 4.1 shows the output

generated during category building, the concepts and types that were extracted and used as the

building blocks for the selected categories. When we build categories, the records or documents are

automatically assigned to categories if they contain text that matches an element of a category's

definition.

Table 4: Negative type

Table 4.1: Negative type concept 1, concept 2 extraction



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Figure 3: category web of patient feedback data

Table 5: category type proportion and the corresponding document

Figure 4: sentiment analysis output generated based on patients feedback data related to “Negative

functioning”



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Figure 5: sentiment alysis output generated based on patients feedback data related to “Negative

functioning- products”


feeling”

Negative budget


budget”



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In Table 4.1.the Concept tab displays the set of concepts that were extracted. Concepts are presented

in a table format with one row for each concept. The objective is to select which concepts will be

used for scoring. In some cases, a concept represents the concept name as well as some other

underlying terms associated with this concept, such as inflected forms, variant terms or synonyms.

Figures 3 and 4 present the document/record overlap for the categories to which the

documents/records belong according to the selection in the other panes. If category labels exist, these

labels appear in the graph. The thickness of the line denotes the number of common documents or

records they have that represents the high traffic interactions between the concepts. It is clear that

patient to nursing staffs have thick lines which was already proved by the decision tree model output

(i.e. the area code 16). In a simple terminology, this represents that there are maximum comments

related to nursing staffing. Figures 5, 6, 7,8 show the sentiment analysis output that clearly reveals

the dissatisfaction elements related to function (infrastructure related), service support (front office

service), budget(expenditure at the hospital). It is clear that there is a high association between the

nursing services and patients relative to other aspects such as medicines, occupation/staff etc. The

sentiment analysis graphs on Figures 5, 6, 7 and 8 reveal that there are complaints related to hospital

cost/expenditure, in-patient facilities with respect to the hospital infrastructure such as air-condition

systems, toilets, fans, beds, etc. The factors related to other dominant reasons were also revealed by

the text mining results which helped the hospital management to take the appropriate corrective

actions. The complete output is not shown in order to preserve confidentiality for patients, doctors,

nurses and staff members

5. KEY RECOMMENDED SOLUTIONS RECOMMENDED AND IMPLEMENTED BY HOSPITAL

MANAGEMENT TEAM

1. Sentiment analysis index has been created and deployed in the hospital management

dashboard itself for the key branch where there were more number of complaints. This index

dynamically gets updated on daily basis as and when the patient‟s feedback management system gets

updated with new feedback entries.

2. Customized training programs was conducted for the target nurses whose service was poor

3. Automated Queue system was deployed for proper bed allocation.

4. Language translators were made available to overcome the problems regarding the language

barrier for non-local or international patients.

5. Proper training was given to all the admission staff to speed up the process and properly

guide patients.

6. Cross functional meetings were conducted with all the kitchen staffs to brief them to be more

cautious while preparing patients‟ food. Production in-charge had been briefed to ensure that taste

and quality of food would be maintained.

7. Departmental meeting was conducted for service staffs, to ensure that timeliness of service

was adhered to.

8. Food for emergency patients was served on scheduled timings. The emergency nurses in

charge had been instructed to serve proper diet to patients.

9. Patients were educated about the availability of floor coordinators in the floors to clarify their

doubts.

10. Admission staffs were trained to behave with patients in a friendly & cordial way.

11. Patients were educated about the waiting areas in the different places.



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12. Proper inventory management system was followed to optimize the reorder level and

quantity.

13. Regular on job refresher training programs were conducted.

14. Preventive healthcare classes scheduled for free for patient‟s relatives(as nominated by

patients)

All the improvement measures were implemented during the improve phase and continuous review

of the different solutions was carried out. All levels of staff involved in the hospital were trained to

follow mistake proofing techniques to carry out routine surveillance of various processes. Clinical

pharmacology personnel monitored the processes with constant vigilance on all the activities of

medication management. Periodic meetings were scheduled to review the progress of the

improvement measures and their impact on the overall business objectives.

6. RESULTS AND DISCUSSION

Patient‟s complaints tracked using the I-MR control chart, from the chart, it is clear that the

reduction of patient‟s complaints started from June 2013 onwards, process shift taken place and the

new control limits were computed . Complaints related to Nursing have been completely eliminated

post implementation of corrective actions. In addition to nursing related complaints the other

immediate complaints such as billing related issues, front office issues, medical services, or PHC

also got reduced. The number of complaints was reduced by 72% i.e. from 577 to 158 over a period

of 1 year which is shown in Figure. (8)

Figure 8: control chart representing the state of “patients‟ complaints” before and after solution

implementation

Post implementation of corrective actions, the number of complaints had been significantly reduced

and the same is represented in the control chart. , but also post feedback had recorded 74.77%

compliments (2351) and 6.88% suggestions (216). Some of the novel approaches to capture the

immense insights into customer experience were management summary dashboard, indices summary

dashboard, departmental scorecard, daily pulse, top ten customer irritants and trend charts,

comparative score cards, qualitative reports on departmental performance, case aging report and

Service Level Agreement for each head of the department.



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7. CONCLUSION

The role of text analytics is increasing at exponential rate with the growth of unstructured data.

Leveraging this in healthcare industry like demonstrated in this case study helps improving the

patient satisfaction and quality of care. Using the decision tree classification modelling technique we

were able to specifically target the departments requiring corrective action implementation without

overspending the training costs. Using text analytics the hospital management team understood each

and every patient feedback and the nature of the complaint. Also, the patient concerns about several

attributes causing dissatisfaction were understood from the voice of the customer which helped in

establishing the CTQ characteristics. Hypothesis testing was used to show that the financial, clinical,

operational, service and safety attributes had a significant impact on patient satisfaction and that it

differs from each patient group. Considerable amount of time was saved by identifying the non-value

adding activities and eliminating them. As a result, the waiting time of the patient had reduced

significantly resulting in increased patient satisfaction. Further, it was proved that the patient

satisfaction was positively correlated with the patient loyalty. Also patient loyalty was positively

correlated with the hospital performance. As a result, the rate of patients with loyalty intention

started increasing hand in hand with referral index of the hospital. This directly improved the

operating profit, operating profit per patient, revenue and revenue per patient.

8. SCOPE FOR FUTURE WORK

The scope for future work for this case study is enormous, for example: creating a “Patientslikeme”

digital network for any hospital in India to establish the connected experience infrastructure which

gives the world class quality care and transparency across all departments of all levels to the top

level management in real time. This paper can also lead the top executives of the hospital industry to

deploy social media monitoring tools to further capture the patients „feedback (comments,

suggestions, experience, satisfaction level) in real time so that it becomes beneficial to the society as

well as the entire healthcare community. Furthermore, the decision tree classification technique

(C5.0) would further help in building predictive models to identify the potential area of issues and

problematic departments based on the patient‟s complaints and also helps the hospital management

team to identify and appreciate the best doctors and staff based on the patient‟s feedback data.

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