Health Care

HEALTH CARE OPINION SURVEY

ABSTRACT The research deals with the hospital care with its patients and also show the factors influencing the choice of a hospital over the other hospital. The research has been done to find the students health care awareness. The factors involving the visiting of a hospital and also visiting the dentist . The students chosen for this are between the age group of 20-25. There has been much debate over the merits of consumer-directed health plans (CDHPs), yet there is little empirical evidence of their influence on health care use. Participants Individuals from student groups from all courses in the vit campus Main outcome measure Corelation between the various factors has been taken in finding the relationship among the factors and make hypothesis through parametric and non-parametric test.

-------------------------------------------------------------------------------------------------------------------* Assistant Professor (S.G.), VIT Business School, VIT University, Vellore – 632 014. ([email protected]) ** MBA Student, VIT Business School, VIT University, Vellore – 632 014. ([email protected])

Introduction

Clinical guidelines often recommend that healthcare professionals should involve patients in decisions about screening, treatment, and other interventions, to help them to arrive at informed choices. Patient decision aids are designed to support than replace patient-practitioner interaction patients in this process. Through these findings ,there is a significant relationship between the factors deciding the visiting of a particular hospital. At a minimum, patient decision aids provide information about the options and their associated relevant outcomes

Objective To develop a set of quality criteria for patient awareness and preference of hospital . . The article deals with the student preference of hospital in the locality of katpadi and also find the various components of customer satisfaction. The article deals with health care service of the health centre with other private health centre. Income, pressure from family, cleniness, modern diagnostics and various factors in determining the visits of the students to a hospital.

RESEARCH METHODOLOGY

The research design opted was descriptive in nature. The research was conducted in Tamil Nadu State, India. The data type was primary and was collected with the administration of questionnaire (close ended). Sample size was 300 respondents. Structured questionnaires were filled by students using the probability sampling technique of the stratified method and the collected data was analysed with the help of SPSS 7.5. The various techniques used for data analysis are correlation, chi-square, oneway annova and Kruskal Wallis test.

LITERATURE REVIEW Consumers rely on a range of information sources and perspectives by Claudia L. Schur and Marc L. Berk A growing body of literature confirms the value of electronic health records (EHRs) in improving patient safety, improving coordination of care, enhancing documentation, and facilitating clinical decision making and adherence to evidence-based clinical guidelines. Opinion polls continue to show that the students has major concerns about the health care system. oUT-OF-POCKET COSTS Account for approximately one fifth of health expenditures and have increased markedly during the past decades. Unmet medical needs could be of various factors like cost factor, transportation and many more reasons. Americans want to see coverage expanded, but their beliefs about the relative roles of government, employers, and consumers vary. by Marc L. Berk, Daniel S. Gaylin, and Claudia L. SchurThe intensity of acute care hospital use by the chronically ill, measured by days spent in the hospital and inpatient physician visits, varies more than twofold among states and regions.1 Previous research has shown

that Medicare spending is inversely associated with technical quality measures among states andHospital Referral Regions (HRRs) defined by the DartmouthAtlas project.2 Health issues are on Americans’ minds, but they are not the top priority in 2006. by Robert J. Blendon, Kelly Hunt, John M. Benson, Channtal Fleischfresser, and Tami BuhrThere is also evidence that per capita use of acute care hospitals may be associated with patients’ ratings their inpatient experiences.

DATA ANALYSIS ONE WAY ANOVA ANALYSIS: If the F probability value in the ANOVA table is less than 0.05, we reject the null hypothesis( at the 95% confidence level) that the category of cost difference significant impact on the skill. From ,the output table for one way ANOVA we see that the probability value of “F” is .000. Therefore ,we reject the null hypothesis and conclude that the category of cost difference has significant impact on skill and competency of the staff.

Oneway De scriptiv e s

N CST.DIFF

SKILL

vry.sat sm.wht Total

172 128 300

Mean 1.10 2.30 1.61

Std. Deviation .30 .46 .70

Std. Error 2.28E-02 4.05E-02 4.05E-02

95% Confidence Interval for Mean Lower Upper Bound Bound 1.05 1.14 2.22 2.38 1.53 1.69

Minimum 1 2 1

Maximum 2 3 3

ANOVA Sum of Squares CST.DIFF

Between Groups Within Groups Total

Mean Square

df

105.331

1

105.331

42.039

298

.141

147.370

299

F 746.667

Sig. .000

CORRELATION

The values in the correlation table are standarised,and range from 0 to 1(+ve and _ve).looking at the last column . we find that favourite hospital & performance difference variables are highly correlated. Correlations Corre lations Pearson Correlation Sig. (2-tailed) N

FAV.HOSP PER.DIFF FAV.HOSP PER.DIFF FAV.HOSP PER.DIFF

FAV.HOSP PER.DIFF 1.000 .725** .725** 1.000 . .000 .000 . 300 300 300 300

**. Correlation is significant at the 0.01 level (2-tailed).

Kruskal-Wallis Test HYPOTHESIS- The value is coming which is greater than .050. Therefore null hypothesis is accepted. Hence recogniation has a impact on placement

NPar Tests

De scriptiv e Statistics N PER.DIFF MDRN.DGN

300 300

Mean 2.23 1.67

Std. Deviation .68 .77

Minimum 1 1

Maximum 3 3

Kruskal-Wallis Test

Ranks

PER.DIFF

MDRN.DGN y n nt.sur Total

N 155 90 55 300

Mean Rank 182.77 116.00 116.00

Te st Statisticsa,b Chi-Square df Asymp. Sig.

PER.DIFF 53.346 2 .000

a. Kruskal Wallis Test b. Grouping Variable: MDRN.DGN

The value is less than .05 hence null hypothesis rejected. CRAMER’S V It is a variation of of the phi correlation co-efficient. Generally, the association between the row and column variable in the cross-tabulation is weak(close to 0) or strong (close to 1).

Symme tric M e asure s

Nominal by Nominal

Phi Cramer's V Contingency Coefficient

Value .897 .897

Approx. Sig. .000 .000

.668

.000

N of Valid Cases

300

a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis.

Crosstabs

Case Proce ssing Summary

N SKILL * CST.DIFF

Cases Missing N Percent

Valid Percent 300

99.7%

1

.3%

Total Percent

N

301

SKILL * CST.DIFF Crosstabulation Count y SKILL

vry.sat sm.wht

155

Total

155

CST.DIFF n 17 90 107

nt sure 38 38

Chi-Square Te sts

Value Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases

241.546

Asymp. Sig. (2-sided)

df a

2

.000

315.721

2

.000

213.708

1

.000

300

a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 16.21.

Total 172 128 300

100.0%

Dire ctional M e asure s

Nominal by Nominal

Lambda

Goodman and Kruskal tau

Symmetric SKILL Dependent CST.DIFF Dependent SKILL Dependent CST.DIFF Dependent

Value .736

Asymp. a Std. Error .034

Approx. T 12.005

Approx. Sig. .000

.867

.034

10.888

.000

.621

.040

11.339

.000

.805

.037

.000

.525

.037

.000

b

c c

a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis. c. Based on chi-square approximation

Chi-square The Chi-squared test is carried out at 90% confidence level( equivqlent to 100-90 divided by 100 or .10 significance level). EXPLANATION OF PEARSON’S CHI-SQUARE Chi-Square Tests Asymp. Sig. (2-sided) Pearson Chi-Square

-

.000

Is a significant relationship between cost difference and skill?The small value of chi-square test states that there exists a significant interrelationship between dependent and independent variables.

EXPLANATION FOR CONTIGENCY COEFFICIENT AND LAMBDA The contingency coefficient gives us the measure of output. If value closes to “0”, there is no strong correlation between the two variables. If the value ranges between .5 and 1,there exist a strong correlation.

Contingency Coefficient - .00(between skill and cost difference) .00(between same hospital and skill) .00(between patient satisfaction and modern diagnosis)

LAMBDA CST.DIFF Dependent - 1) 0.62 (between skill and cost difference) Lambda is a measure of reduction in error in measuring the association between the two variables. For 1 and 2 there is ahigh percent relation, whereas 3 has a moderate value,it has moderate relationship between two variables.

RESULT OF CROSS TABULATION: The contingency coefficient value is greator than +.5 then the variables are strongly associated. But here it is less than .5 hence the variables are not strongly associated. Crosstabs

Case Proce ssing Summary

N SKILL * CST.DIFF

Valid Percent 300

Cases Missing N Percent

99.7%

1

.3%

Total Percent

N

301

SKILL * CST.DIFF Crosstabulation Count y SKILL Total

vry.sat sm.wht

155 155

CST.DIFF n 17 90 107

nt sure 38 38

Total 172 128 300

100.0%

Chi-Square Te sts

Value Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases

241.546

Asymp. Sig. (2-sided)

df a

2

.000

315.721

2

.000

213.708

1

.000

300

a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 16.21. Dire ctional M e asure s

Nominal by Nominal

Lambda

Goodman and Kruskal tau

Symmetric SKILL Dependent CST.DIFF Dependent SKILL Dependent CST.DIFF Dependent

Value .736

Asymp. a Std. Error .034

Approx. T 12.005

Approx. Sig. .000

.867

.034

10.888

.000

.621

.040

11.339

.000

.805

.037

.000

.525

.037

.000

b

a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis. c. Based on chi-square approximation

Discriminant

c c

Analysis Case Proce ssing Summary Unweighted Cases Valid Excl Missing or uded out-of-range group codes At least one missing discriminating variable Both missing or out-of-range group codes and at least one missing discriminating variable Total Total

N 300

Percent 99.7

0

.0

0

.0

1

.3

1 301

.3 100.0

With a hi gh wilk’s lambda of .542 which is reasonably good. Best predictor is gender Group Statistics

PER.DIFF yes no not sure Total

GENDER AGE GENDER AGE GENDER AGE GENDER AGE

Valid N (listwise) Unweighted Weighted 43 43.000 43 43.000 145 145.000 145 145.000 112 112.000 112 112.000 300 300.000 300 300.000

Analysis 1

Variable s Failing Tole rance Te ast

AGE

Within-Groups Variance .000

Tolerance .000

Minimum Tolerance .000

All variables passing the tolerance criteria are entered simultaneously. a. Minimum tolerance level is .001.

Summary of Canonical Discriminant Functions

Eigenv alue s Function 1

Eigenvalue .845 a

% of Variance 100.0

Cumulative % 100.0

Canonical Correlation .677

a. First 1 canonical discriminant functions were used in the analysis.

Wilks' Lambda Test of Function(s) 1

Wilks' Lambda .542

Standardized Canonical Discriminant Function Coe fficie nts

GENDER

Function 1 1.000

Structure M atrix

GENDER

Function 1 1.000

Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function.

Chi-square 181.980

df 2

Sig. .000

Canonical Discriminant Function Coe fficie nts

GENDER (Constant)

Function 1 2.950 -3.834

Unstandardized coefficients

Functions at Group Ce ntroids

PER.DIFF yes no not sure

Function 1 -.885 .946 -.885

Unstandardized canonical discriminant functions evaluated at group means

Classification Statistics

Classification Proce ssing Summary Processed Excluded

Used in Output

301 Missing or out-of-range group codes At least one missing discriminating variable

Prior Probabilitie s for Groups

0

1

300

PER.DIFF yes no not sure Total

Prior .333 .333 .333 1.000

Cases Used in Analysis Unweighted Weighted 43 43.000 145 145.000 112 112.000 300 300.000

a Classification Re sults

Original

Count

%

PER.DIFF yes no not sure yes no not sure

Predicted Group Membership yes no not sure 43 0 0 55 90 0 112 0 0 100.0 .0 .0 37.9 62.1 .0 100.0 .0 .0

Total 43 145 112 100.0 100.0 100.0

a. 44.3% of original grouped cases correctly classified.

CONCLUSION Students research show that there is ahigh correlation between choosing a favourite hospital and skill & competency of the staff. These phenomena,when interpreted in the light of research showing that illness- and severity is higher in regions with greater use of acute care hospitals. Factors are highly correlated like – receiving care from hospital,modern operating room services,convenience location,cleaniness etc.

Whereas the dental care survey shows that the those who don’t go to doctors for regular checkup the common reason for not going be – transportation,cost of care and some donot feel the need for going to doctor. Family pressure is also an important pressure for visiting the doctor for health care problems.

BIBILOGRAPHY 1. J.E.Wennberg et al., Tracking the Care of Patients with Severe Chronic Illness: TheDartmouthAtlas ofHealth Care 2008 (Lebanon, N.H.: Dartmouth Institute for Health Policy and Clinical Practice, 2008). 2. K. Baicker and A. Chandra, “Medicare Spending, the PhysicianWorkforce, and Beneficiaries’ Quality of

Care,” Health Affairs 23 (2004): w184–w197 (published online 7 April 2004; 10.1377/hlthaff.w4.184). 3. J.E.Wennberg et al., “Evaluating the Efficiency ofCalifornia Providers in Caring for PatientswithChronic Illnesses,” Health Affairs 24 (2005): w526–w543 (published online 16 November 2005; 10.1377/hlthaff .w5.526); and L.C. Baker, E.S. Fisher, and J.E.Wennberg, “Variations in Hospital Resource Use forMedicare and Privately Insured Populations inCalifornia,” HealthAffairs 27, no. 2 (2008): w123–w134 (published online 12 February 2008; 10.1377/hlthaff.27.2.w123). 4. SeeU.S. Department ofHealth and Human Services, Hospital Compare— AQuality Tool forAdults, Including People withMedicare, http://www.hospitalcompare.hhs.gov (accessed 22 October 2008). 5. For the methodology, see ibid.