improving patient care in hospital in india by monitoring influential parameters

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International Journal of Healthcare Management

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Improving patient care in hospital in India bymonitoring influential parameters

Sanjay Mohapatra & Siddharth Murarka

To cite this article: Sanjay Mohapatra & Siddharth Murarka (2016): Improving patient carein hospital in India by monitoring influential parameters, International Journal of HealthcareManagement

To link to this article: http://dx.doi.org/10.1080/20479700.2015.1101938

Published online: 10 Feb 2016.

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Improving patient care inhospital in India by monitoringinfluential parameters Correspondence to:

Sanjay Mohapatra, XavierInstitute of Management,Bhubaneswar XavierUniversity, 105 GaganAwaas Apt, Bhubaneswar,[email protected]

Sanjay Mohapatra and Siddharth Murarka

Xavier Institute of Management, Bhubaneswar Xavier University, 105Gagan Awaas Apt, Bhubaneswar, India

Abstract

Purpose: The purpose of this paper is to identifydifferent parameters that govern the performanceof hospitals in Indian context. Performance of hospi-tal implies that patients are satisfied with the qualityof treatment, and cost of treatment is low. Severalliteratures were reviewed for this purpose, and refer-ences were drawn from many countries around theglobe to evaluate the extent to which differentfactors have an impact on the healthcare delivery.Methodology: The methodology involved collect-ing data through primary survey. Through literaturereview, a priory factors that impact quality of health-care were determined. After determining thefactors, a theoretical framework was developed.Using this framework, a survey of 162 respondentswere conducted and data analysed by structuredequation modelling to determine the key factorsthat impact performances of healthcare in India.This was done by analysing the standardized coeffi-cients of the variables that explained a majority ofthe variations of the dependent variable.Findings: From the analysis, it was found that tech-nology helped in integrating processes whichreduced the cost of healthcare. Also, economies ofscale, availability of skilled doctors, nurses, andstaff improved quality of healthcare. Using technol-ogy to maintain electronic healthcare records (EHR)improved consistency, predictability of healthcareservices.Practical implication: The findings, whenimplemented, can be used by hospitals to focuson the key factors to improve their performance,reduce costs, and enhance performance. This willalso help in prioritizing the resource allocations ina hospital.Originality: The framework developed is based onprimary survey and is original and not been repli-cated or copied from other sources. This aims toprovide guidance for healthcare providers to assesstheir performance and thereafter improve upon it.

Keywords: Healthcare, Hospitals in India,Healthcare framework, Parameters in healthcareperformance

Introduction

Health is a state of complete physical, mental, andsocial well-being and not merely an absence ofdisease or infirmity.1 Healthcare has been definedas a multitude of services rendered to individuals,families, or communities by health service pro-fessionals for promoting, maintaining, or restoringhealth.2 The quality of healthcare service is impor-tant as that determines the sustainability of hospi-tals.3 However, sometimes the cost of qualityhealthcare is high and it becomes expensive forpatients.4 Hence, it is important to identify corehealthcare processes, and associated supportivequality parameters, acting in synergy to achievethe best results for patients and to control costs.However, several cases have been reported whereinitiatives on healthcare have failed to get itsdesired results.12 In this paper, we have attemptedto find the parameters that will impact quality ofhealthcare and have provided a solution that willreduce cost through process and technology inte-gration. After reviewing the healthcare-quality lit-erature, has determined several important factorsthat impact quality of healthcare were determined.Using structured equation modelling (SEM), theparameters that have high impacts on quality andcost of healthcare were determined. The findingsof the analysis can be used in other hospitals andwhich will benefit patients (in terms of cost ofhealthcare).

Literature review

Rajagopal5 provided a conceptual framework toexamine how far variables such as health delivery,

1© Taylor & Francis 2016DOI: 10.1080/20479700.2015.1101938 International Journal of Healthcare Management 2016

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mailto:[email protected]



http://orcid.org/0000-0001-9514-862X

quality administration, accessibility of medicalinformation, precision in medical test, and reductionof workload (independent variables) are related tohealthcare technologies (dependent variable). Thestandardized coefficient values were found to bethe highest for health delivery and precision inmedical tests. This was described in terms ofreduction of cost of healthcare services, store ofmedical records, diagnosis of diseases, maintenanceof personal privacy, quick medical results, accessi-bility of patients’ information, savings of consul-tation time, trustworthy service needs,maintenance of secrecy, and reducing the treatmenttime. However, the advantages of health infor-mation technology (HIT) could not be fully utilizedin many other areas of healthcare services (e.g.bringing quality administration, accessibility ofmedical information, etc.). Also, there was a nega-tive correlation between accessibility of medicalinformation and quality administration with tech-nology indicating the patients were unable toaccess medical information from the hospitalsaccording to their requirements. Policy changessuch as levy of ‘user fees’ for sharing informationmay be adopted.Electronic medical records (EMRs) contain costs

by better coordinating care, rescuing patients frommedical errors once they occur.6 The study high-lighted that the spending per patient safety eventwas lower for hospitals implementing EMRs,however, overall EMR surgeries were more expens-ive. EMR can also improve administrative decision-making and the allocation of scarce healthcareresources.7 EMRs reduced the excess costs of hospi-tal acquired conditions (HACs), decreased readmis-sion rates and reduced excess death rates. Theresults also show that investing in IT is indeed costeffective. Hillestad et al.8 and his colleagues demon-strated the use of a simulation model of HIT adop-tion and scaling literature-based HIT effects todetermine the potential cost savings. The largestsource of savings ($77 billion per year) appeared tocome from reducing hospital lengths-of-stay,nurses’ administrative time, drug usage in hospitals,and drug and radiology usage in the outpatient set-tings. These savings would only be realized afterwidespread implementation of HIT by providers,and associated process changes and resourcereductions had taken place. The potential savingsfrom adopting the EMR systems outweighed thecosts relatively quickly during the adoption cycle.Key barriers in the HIT market directly impedeadoption and effective application of EMRsystems; these include acquisition and implemen-tation costs, slow and uncertain financial payoffs,

and disruptive effects on practices. Walker exploredthe qualitative and economic implications ofhealth care information exchange and interoperabil-ity (HIEI). Giving clinicians access to data abouttheir patients’ care from providers outside theirorganizations would likely result in fewermedical errors and better continuity of care.However, electronic exchange of clinical databetween organizations is nascent, and minimaldata exist about the clinical impact it wouldbring. Zhivan and Diana9 suggest the benefits ofEMR adoption are more likely to outweigh thecosts of adoption for hospitals with a greaterdegree of inefficiency. Byrne et al.10 suggestedthat veteran affairs’ (VA) investment in VeteranHealth Information Systems was associated withsignificant value through reduction in unnecessaryand redundant care, and improvements in carequality. The investments in four health IT systemsestimated $3.09 billion in cumulative benefits netof investment costs.Bardhan and Thouin3 regression estimation

results, based on a large panel sample of US hos-pitals tracked over a 3-year period from 2004 to2006, indicate significant differences in the usageof HIT systems and their association with thequality of healthcare delivery processes and hospi-tal operational expenses. Clinical informationsystems, which include EMR and clinical decisionsupport systems, were associated with significantimprovements in the overall hospital processquality. These results suggest that IT investmentsin EHRs and systems for electronic patient sche-duling and human resource management have apositive impact on the overall quality of healthcaredelivery. Unlike prior studies which primarilyfocus on the overall hospital outcomes (such asmortality and readmission rates), Bardhan andThouin3 explores the impact of health IT on evi-dence-based measures of process quality whichrepresent intermediate metrics that are antecedentsof hospital-level quality outcomes. The resultssuggest that while healthcare IT offers a significantvalue in terms of their impact on the overallquality associated with healthcare processes, theireffect on hospital operating expenses needs to befactored into consideration when makingimplementation decisions. Analyses of the oper-ational expense data over a 3-year horizonsuggest that financial and human resource man-agement systems have a significant impact interms of lower hospital operating expenses, whileimplementation of clinical information systemsand scheduling systems is associated with higheroperating expenses.

Mohapatra and Murarka Improving patient care in hospital in India by monitoring influential parameters

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However, the recent study by Agha1 indicatedthat there has been increase in medical expenditure(1.3%) after HIT adoption; this was due to highercharges for inpatient hospital treatment.Furthermore, the quality of hospital care, asmeasured by mortality rate, readmissions, adversedrug events and complications is unaffected byHIT investment. Further research needs to be con-ducted to study the positive social returns fromHIT investment.Despite the various benefits demonstrated by HIT

systems, Siska and Tribble11 highlight various chal-lenges to the adoption of HIT in hospitals and healthcentres. Significant number of challenges, includingbut not limited to, financial, cultural, technical, stra-tegic, and structural, have restricted the investmentsto basic automation rather than integration of first-generation, multifunctional, interoperating HITsystems. Various solutions that have been proposedto address these challenges in the context of drugdistribution are based on adoption of well-inte-grated technology solutions centred on an EHR.Pharmacists must drive the change managementprocess. Anwar and Shamim12 have provided aclear understanding about the adaptation barriersof health technology faced by developing societies.The information about barriers would be useful forpolicy makers to decide about the particular tech-nology. These barriers are divided into six groups,i.e. ICT infrastructure, cost and time, national pol-icies towards HIT, social and cultural, educational,organizational, and ethical barriers. HIS cell orhealth technology projects were found lacking inbudget. The time period, which is required forwell implementation of the specific health technol-ogy, was found to be quite long. Good collaborationis required between health informatics personneland professional groups for a better understandingby training people in both the fields of informaticsand health. Increased leadership and involvementfrom physicians is necessary due to their role aspatient advocates and stewards of scarce healthcareresources.7

To measure various parameters that impacthealthcare services in hospitals, the study by KerJ. provided an insight into the measurement ofthe effectiveness of two prescribing technologies,non-carbon required and digital scanning technol-ogy. The parameters used to compare the two tech-nologies were queue time, order entry time,outgoing delay time, and oncoming transit time.For the cost analysis, the parameters selected weretime spent by pharmacists, prescription papers,the introductory costs, and paper shredding costs.The important benefit of digital scanning

technology is that pharmacy productivity andprofits were increased through the use of thedigital scanning technology. The use of digitalscanning technology in the order entry facilityimproves the turnaround time and cost spendingduring the medication distribution process. The fra-mework proposed by Richard and co-workers8

forms a basis for performance measurement of avariety of agencies and organizations engaged inthe practice of public health or of a specific publichealth organization.

Stargardt and Schreyögg17 have made anattempt to analyse the impact of healthcare infra-structure on the quality of health services anddelay in diagnosis. For availability of healthcareinfrastructure, possible measures are physiciandensity, defined as number per square kilometre,quantity of diagnostic equipment such as MRI,CT/PET scanners, etc. Bhargava et al.2 whilemeasuring the impact of infrastructure on childmortality has used parameters such as number ofgovernment hospitals, number of private hospitals,average qualified staff, monthly supply of pills, etc.Blankart13 has used other parameters such as theintensity of care defined by number of nursingstaff per hospital bed which tends to reduce thedelay in diagnosis. The parameters were found tohave a higher impact in rural areas than in urbanareas.

Mohammad14 has outlined the parameters tomeasure the quality of healthcare. They aregrouped as: core quality of healthcare (professional,ethical, and technical competency), associated sup-portive quality (satisfaction of related interestgroups, i.e. family, friends, etc., managing internalcustomers, i.e. staff ). Hasin et al.15 has collecteddata about various parameters that affect customersatisfaction such as service of doctors, nurses, staff,cleanliness, service rate, etc.

Theoretical framework

Based on the literature review, a theoretical frame-work (Fig. 1) was developed composing thevarious parameters that affect healthcare perform-ance across various hospitals around the globe.The model has been depicted in Fig. 1.

To analyse the impact on hospital performance,the following framework (Fig. 1) was developed.Of the 29 variables that were identified, the relevantones were selected and grouped as shown in the fra-mework shown below. Fifteen variables were ident-ified that impact the performance of hospitals. These15 variables were again clubbed into four broad sub-groups so that their relevance during survey


3International Journal of Healthcare Management 2016

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assessment could be more prominent. These wouldform the independent variables for the survey. Toassess the performance, six dependent variableswere selected based on the above literature reviewas follows (Fig. 2).

Dependent variables1. Reduced cost of healthcare services2. Easy storage and accessibility of medical

records of patients

3. Promptness in diagnosis of disease4. Personal privacy and secrecy5. Reduction in time for medical results6. Reduced consultation and treatment time

Independent variables• Health infrastructureo Nursing staff per hospital bedo Number of hospital bedso Physician density

Figure 1: Framework for variables affecting the performance of healthcare services.



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oQuantity of diagnostic equipment (CRs, PETs)o Qualification of staffo Monthly supply of medicines

• Healthcare ITo Electronic patient scheduling/human

resource managemento Electronic health record of patientso Clinical decision support systems

• Quality of healthcare serviceo Cleanlinesso Service of doctorso Clinical decision systemso Food, service rate, courtesy, etc.o Professional/ethical/technical competency

• Quality of administrationo Quality of administration

Figure 2: Framework segregating the variables into independent and dependent variables.



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Objective

The present healthcare system in India is evolving,and efficient use of resources has become a keyfactor in determining the allocation of resources.The objective of this study was to analyse factorsthat determine healthcare performance and cost,and their impact on hospitals in India.

Methodology

For collecting the data, a pilot survey was con-ducted. Based on feedback, the questionnaire wasmodified. The modified questionnaire was used inan online survey portal for collecting data. Therespondents were selected from hospitals locatedin Bhubaneshwar. The respondents includedpatients who had visited these hospitals in thepast 1 year, and doctors and staff working in thesehospitals. The data were collected over a periodspanning 6–8 weeks. The total sample size obtainedafter initial data screening was 162 comprising threehospitals namely Kalinga Hospital, Apollo Hospital,and CARE Hospital. These hospitals are located inBhubaneswar, a state capital, where there is a mixof private and public organizations along withstate and central government offices. As a result,the lifestyle and income16 parity were consideredto be similar to other states in India. Proximity ofthe hospitals to the researchers also helped in col-lecting data within reasonable time period.

Kalinga Hospital

Kalinga Hospital Limited (KHL) is one of the pio-neers of a state of the art Multi Super SpecialtyHospitals, providing preventive, curative and reha-bilitative services to the patients from all the econ-omic and social strata, care to the people of Odishaand adjoining regions. Set up by a visionary philan-thropic group of 54 Non Resident Indians (NRIs)under the name of Hospital Corporation of Orissa(HCO), a company registered in USA. The hospitalhas been registered under companies Act 1956 on2 May 1990.As an ISO 9001-2008 institution for rendering

Multi Super Specialty Health Care Services, KHLhas around 250 beds to provide healthcare servicesat reasonable rates to its patients. The centre of excel-lence includes upgraded 24×7 Emergency andTrauma Care Unit with a 42-bed critical care unitto meet tertiary health care standards. Also,Cardiology & Cardiac Surgery Dept, Neurology &Neurosurgery Unit, Medical & SurgicalGastroenterology Dept with Modern Machines –Hemoclip, APC, ERCP, etc. and the largest no. of

dialysis machines are available in KHL. AdvancedOrthopaedic Surgeries – Endospine & Total Kneeor Hip Replacement, etc. are also available underone roof along with the most modern diagnosticand therapeutic investigation such as EP Study, CTguided FNAC, Mammography, 500 Slice CT Scan,1.5 Tesla MRI, Colposcopy, etc.In addition, the hospital has facilities for full range

of treatment in areas such as general medicine,general and laparoscopic surgery, geriatrics, dental& maxillofacial surgery, dermatology & cosmeticsurgery, physical medicine & rehabilitation, oph-thalmology and ENT, interventional radiology – toname a few. The institute also provides round-the-clock service of pharmacy and ambulatory services.

Apollo Hospitals, Bhubaneshwar

Apollo Hospitals, Bhubaneshwar, the 49th hospitalof the group was inaugurated on 5 March 2010.This Health Care Institution is a 350-bed tertiarycare hospital with state-of-the-art technology,spread over a campus area of about 7.5 acres. TheHospital’s Outpatient Department (OPD) has 37consultation chambers for consultants of all depart-ments. The OPD is supported by treatment/minorprocedure rooms along with outpatient services inophthalmology, ENT, dermatology, dentistry, etc.Medical and surgical cardiac sciences, oncology,

neurosciences (neurology and neurosurgery & neu-rophysiology), urology, nephrology, rheumatology,endocrinology, etc. are some of the departmentsthat have state-of-the-art facilities here.

CARE Hospitals, Bhubaneswar

CARE Hospitals, Bhubaneswar, was inaugurated on24 September 2007; full-fledged services startedfrom October 2007 onwards. The 100-bed largest,private, multi-superspecialty corporate hospital inthe city, has experienced doctors, well-trained para-medical professionals, and state-of-the-art equip-ment. The intensive care units are customized toeffectively manage a range of specialized problems.The critical intensive care unit (CICU) is equippedwith computerized bedside monitoring facility.Cardiac surgery and cardiology patients are pro-vided with specialized beds in post-operativeunits. The state-of-the-art intensive care unit hascardiac defibrillators, advanced ventilators, infusionpumps, transvenous & transthoracic pacing, pulseoximeters, continuous oxygen supply, invasiveand non-invasive pre-monitoring systems, tempor-ary pacemakers, nebulizer and blood gas analyser,24-hour cardiologist, 24-hour anaesthetist, andspecially trained medical and paramedical staff.



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The Cineless Cardiac Catheterisation Lab atCARE Hospitals enables highly qualified interven-tional cardiologists to perform complex cardiac pro-cedures effectively, with accuracy and minimumamount of radiation. The hospital also has anactive paediatric cardiac intervention facility. Thecardiac surgery services offer treatment for alltypes of surgically repairable heart diseases – fromcoronary bypass surgery in the elderly to congenitalheart diseases in infants and children (1–2 weeksold–18 years of age). The Department ofNephrology, in association with the Department ofUrology, is staffed by highly professional nephrolo-gists and trained dialysis technicians and nurses,capable of taking complete care of routine and emer-gency patients. In addition to routine haemodialysisand peritoneal dialysis, continuous ambulatory per-itoneal dialysis is also offered to patients.Admission, on the day of arrival, is assured on

priority for outstation patients. One attendant is pro-vided accommodation, free of cost, with the patientin the hospital wards. An in-house food and bever-age department, with a cafeteria, caters to therequirements of patients and their familymembers. On prior intimation, patients can bepicked up from the railway station or airport. Theblood bank has been outsourced to CapitalHospital Blood Bank, Bhubaneswar, Hi-TechHospital Blood Bank, Bhubaneswar and JagannathBlood Bank, Bhubaneswar. The emergency room isequipped with the best facilities. It has five beds inthe casualty room, which generally treats cardiaccases. The room has been designed to have a closesemblance to an ICCU for the patients’ require-ments. Emergency services are available 24 hours aday.The methodology involved some of the steps as

mentioned below:

• Preparation of the questionnaire• Conduct pilot survey• Review of the questionnaire after pilot survey• Primary data collection through on-line surveys• Empirical analysis of the data collected• Performing factor analysis to determine signifi-

cant factors that impact hospital performance inAMOS

• Analysing the impact of these factors on theDVs that predict hospital performance

Data collection

Based on the above framework, a questionnaire wasprepared incorporating all the above parameters.The primary data points were collected throughonline surveys distributed across India. Thisresulted in quick data collection of 162 data pointsfrom people who had been to various hospitalslocated across Bhubaneshwar. The data analysishas been performed primarily through simulationusing AMOS and SPSS.

Rule of thumb of goodness of fitCMIN/DF: Avalue below 2 is preferred but between2 and 5 is considered acceptable, where CMIN indi-cates chi-square value and DF is degrees of freedom.

CFI, GFI, and NFI: Avalue equal to or above 0.90 ispreferred.

CFI – comparative fit index; GFI – goodness of fitindex; NFI – normed fit index.

PCFI, PGFI, and PNFI: A value equal to or above0.50 is preferred.

PCFI – Parsimonious Comparative Fit Index;PGFI – Parsimony Goodness of Fit Index; PNFI –Parsimonious Normed Fit Index.

RMSEA: A value less than 0.08 is preferred.RMSEA – root mean squared error of approximation.

Data analysis

Explanatory factor analysis (EFA)After data collection, explanatory factor analysiswas performed on the 15 independent variables todetermine the major factors that impacted hospitalperformance. This was done by using datareduction analysis in SPSS. Maximum likelihoodmethod of factor analysis was chosen. Eigenvalues of greater than 1 were considered allowingSPSS the freedom to determine the number of vari-ables. Promax rotation was used during the analysisand coefficients below 0.4 were suppressed in thepattern matrix. The following results weredetermined:



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Confirmatory factor analysis (CFA)Using the above pattern matrix, the pattern matrixplugin was used in AMOS to generate the modelas shown below. The covariances between the fourfactors were found to be very low, i.e. below 0.2for each of the pairs. The factor loadings for theIVs were found to be very high as shown in thefigure below the lowest being 0.69 for electronicpatient scheduling with healthcare information tech-nology (factor 2). The regression weights for all thefour factors were found to be significant with highloadings, low standard error and significantP-values (<0.05).The CMIN value which should be between 1 and

3 was found to be a conservative 1.506, whereas theCFI was 0.858. The PCLOSE was found to be low asdesired at 0.037. The modification indices werefound to be less than 6 for most of the variablesand hence no other variables were covaried witheach other. A common latent factor was also usedto examine the impact of any external latent factor

or common method bias. The results showed no sig-nificant impact of any latent variable on the modelas they reported no significant change in theregression weights and had low standard error.Hence, were not considered for furtherinvestigation.

Hybrid structural equation modelling (hybrid-SEM)Using CFA, the four factors healthcare infrastruc-ture, healthcare information technology, quality ofhealthcare service and food, service rate, and cour-tesy were determined by using the above mentioned13 independent variables. The correlation betweenthese factors was low which emphasizes the inde-pendence of the factors. Using them, a hybridmodel was developed to study the impact of thesefactors on the dependent variables. The fourfactors were regressed against each of the six depen-dent variables that were considered to evaluate hos-pital performance. The initial model simulated isshown below:

Factor

Pattern matrixa 1 2 3 4

Diag_equip 0.810Physician_density 0.781Qualifcn_Staff 0.753No_of_beds 0.663Supply_medicine 0.649EHR_patients 0.780CDSS 0.689Elec_Patient_Sch 0.569Clinical_decision_sys 0.530Competency 0.853Service_of_Doctors 0.842Cleanliness 0.531Food_ser_rate_courtesy 0.954Extraction method: maximum likelihood.Rotation method: Promax with Kaiser normalization.aRotation converged in five iterations.

Factor correlation matrix

Factor 1 2 3 4

1 1.000 0.253 0.244 0.0542 0.253 1.000 0.445 0.2723 0.244 0.445 1.000 0.2874 0.054 0.272 0.287 1.000Extraction method: maximum likelihood.A high KMOmeasure of sampling adequacy of 0.665 was found. The significance valuewas also negligible. Out of the 15independent variables tested, two were removed as they were found to have incorrect relationship and/or insignificantfactor loadings. The communalities for the remaining 13 variables were found to be significant as shown in the table (allbut one have communality greater than 0.3). The total variance explained by the four extracted factors is more than 60%which is also satisfactory. Hence, the extracted factors are adequate in explaining the variation of the independentvariables.



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Error terms were associated to each of the IVs toaccount for any unobserved variation. The model

was simulated to obtain the following initialregression weights:

Regression weights: (group number 1 – default model)

Estimate S.E. C.R. P Label

No_of_beds <--- Infra 1.000Physician_density <--- Infra 1.215 0.264 4.610 ***Diag_equip <--- Infra 1.126 0.254 4.432 ***Qualifcn_Staff <--- Infra 1.195 0.284 4.200 ***Supply_medicine <--- Infra 1.119 0.288 3.889 ***EHR_patients <--- IT 1.000Elec_Patient_Sch <--- IT 0.691 0.229 3.019 0.003CDSS <--- IT 1.315 0.322 4.085 ***Clinical_decision_sys <--- IT 0.728 0.221 3.290 0.001Cleanliness <--- Quality 1.000Service_of_Doctors <--- Quality 0.890 0.175 5.078 ***Competency <--- Quality 0.991 0.213 4.661 ***Food_ser_rate_courtesy <--- Food_courtsey 1.000



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The CMIN for the above simulation was found tobe acceptable at 1.525 whereas the CFI was 0.796.The modification indices were found to be lessthan 7 for most of the variables. PCLOSE was sig-nificant at 0.004, RMSEA being 0.104.From the above model, the relations which had a

low significance level (P-value greater than 0.05)and/or had incorrect a-priori relationship wereremoved and the model was again simulated inAMOS to obtain a modified model as shown below:

From the above model, the simulation resultshighlighted the impact of the four factors on health-care performance after regressing with the depen-dent variables. It was found that the CMIN was1.431, whereas the CFI was an acceptable 0.813.The significance could be obtained from PCLOSEwhich was found to be low at 0.013. The RMSEAstood at an acceptable value of 0.096.

Results

1. Information technology (IT) was found to havea strong negative correlation (−0.814) withreduced cost of healthcare services which indi-cates that hospitals with greater usage of IThave higher healthcare service charges as com-pared to those who have not implemented IT.

CMIN

Model NPAR CMIN DF PCMIN/

DF

Default model 31 90.366 60 0.007 1.506Saturatedmodel

91 0.000 0

Independencemodel

13 292.537 78 0.000 3.750

Baseline comparisons

ModelNFI

Delta1RFIrho1

IFIDelta2

TLIrho2 CFI

Default model 0.691 0.598 0.869 0.816 0.858Saturatedmodel

1.000 1.000 1.000

Independencemodel

0.000 0.000 0.000 0.000 0.000

RMSEA

Model RMSEALO90

HI90 PCLOSE

Default model 0.104 0.056 0.146 0.037Independencemodel

0.242 0.213 0.272 0.000



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2. IT also had a high impact in maintainingprivacy and secrecy of patients and upholdingconfidentiality of information as the modelindicated a significant relationship betweenthem (P-value = 0.021) with a regression coef-ficient of 0.481. Hence, hospitals where IT hadbeen implemented were better able to maintainprivacy and confidential information of theirpatients.

3. Quality of the healthcare service provided wasfound to have the strongest relationship (1.271)with reduced cost of healthcare services. Thissuggests hospitals with more qualified/com-petent doctors and/or more cleanliness wereproviding services at a cheaper rate. Thiscould be the case in Kalinga hospital whichhas some of the best qualified professionals

working and still manages to provide its ser-vices at an affordable rate.

4. Quality had a strong correlation (0.894) withpromptness of diagnosis of patients which islogical as more qualified and competent doctorswould be better able to diagnose their patientsand thereby reduce treatment and consultingtime which also had a strong dependency onquality of healthcare service with regressionweights of 0.771 and 0.850, respectively.

5. Infrastructure was not found to be a significantfactor influencing hospital performance. Thehighest correlation achieved was with reducedcost of healthcare services with a low loadingof 0.108 indicating hospitals with better infra-structure would be operating on economies ofscale allowing them to reduce their service costs.

Regression weights: (group number 1 – default model)


No_of_beds <--- Infra 1.000Physician_density <--- Infra 1.165 0.251 4.636 ***Diag_equip <--- Infra 1.111 0.245 4.540 ***Qualifcn_Staff <--- Infra 1.184 0.275 4.308 ***Supply_medicine <--- Infra 1.101 0.278 3.954 ***EHR_patients <--- IT 1.000Elec_Patient_Sch <--- IT 0.619 0.177 3.506 ***CDSS <--- IT 0.963 0.219 4.404 ***Clinical_decision_sys <--- IT 0.558 0.170 3.279 0.001Cleanliness <--- Quality 1.000Service_of_Doctors <--- Quality 0.860 0.167 5.156 ***Competency <--- Quality 0.945 0.210 4.490 ***Food_ser_rate_courtesy <--- Food_courtsey 1.000Red_cost <--- Infra 0.141 0.172 0.821 0.412Ease_storage_access <--- Infra 0.002 0.208 0.011 0.991Promt_diag <--- Infra 0.054 0.156 0.346 0.730Privacy_secrecy <--- Infra −0.311 0.188 −1.654 0.098Red_time_results <--- Infra −0.071 0.188 −0.377 0.706Red_conslt_trt_time <--- Infra −0.102 0.176 −0.577 0.564Red_cost <--- IT −0.743 0.264 −2.814 0.005Ease_storage_access <--- IT 0.135 0.275 0.489 0.625Promt_diag <--- IT 0.115 0.207 0.556 0.578Privacy_secrecy <--- IT 0.562 0.252 2.228 0.026Red_time_results <--- IT 0.025 0.248 0.103 0.918Red_conslt_trt_time <--- IT 0.120 0.232 0.515 0.606Red_cost <--- Quality 1.111 0.314 3.543 ***Ease_storage_access <--- Quality 0.750 0.336 2.231 0.026Promt_diag <--- Quality 0.753 0.259 2.914 0.004Privacy_secrecy <--- Quality −0.011 0.286 −0.039 0.969Red_time_results <--- Quality 0.891 0.310 2.872 0.004Red_conslt_trt_time <--- Quality 1.022 0.298 3.428 ***Red_cost <--- Food_courtsey 0.131 0.121 1.081 0.280Ease_storage_access <--- Food_courtsey 0.070 0.147 0.475 0.635Promt_diag <--- Food_courtsey 0.055 0.111 0.500 0.617Privacy_secrecy <--- Food_courtsey 0.224 0.129 1.742 0.081Red_time_results <--- Food_courtsey −0.076 0.133 −0.569 0.570Red_conslt_trt_time <--- Food_courtsey −0.206 0.125 −1.653 0.098



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Analysis with multivariate multipleregression

To further analyse the impact of each of the IVs onhospital performance, a multivariate multipleregression analysis was performed using AMOSon all the DVs. The model simulated is shownbelow:

The error terms with modification indices above12 were covaried together if they were on the samelevel and if they represented the same factor. Errorterms for reduced time for results and reduced con-sultation and treatment times were also covaried asthey are expected to encounter common externalbias. This led to an acceptable CMIN of 2.596 andnegligible PCLOSE value.

Regression Weights: (Group number 1 - Default model)


No_of_beds <--- Infra 1.000Physician_density <--- Infra 1.208 0.257 4.708 ***Diag_equip <--- Infra 1.099 0.246 4.459 ***Qualifcn_Staff <--- Infra 1.162 0.276 4.206 ***Supply_medicine <--- Infra 1.111 0.281 3.954 ***EHR_patients <--- IT 1.000Elec_Patient_Sch <--- IT 0.617 0.178 3.467 ***CDSS <--- IT 0.968 0.221 4.375 ***Clinical_decision_sys <--- IT 0.559 0.171 3.264 0.001Cleanliness <--- Quality 1.000Service_of_Doctors <--- Quality 0.836 0.162 5.172 ***Competency <--- Quality 0.917 0.205 4.478 ***Food_ser_rate_courtsy <--- Food_courtsey 1.000Red_cost <--- IT −0.814 0.277 −2.935 0.003Privacy_secrecy <--- IT 0.481 0.208 2.306 0.021Red_cost <--- Quality 1.271 0.313 4.056 ***Ease_storage_access <--- Quality 0.892 0.234 3.809 ***Promt_diag <--- Quality 0.894 0.185 4.829 ***Red_time_results <--- Quality 0.771 0.208 3.701 ***Red_conslt_trt_time <--- Quality 0.850 0.202 4.205 ***Privacy_secrecy <--- Food_courtsey 0.237 0.122 1.932 0.053Red_cost <--- Infra 0.108 0.167 0.648 0.517



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Results

1. Number of beds had a significant impact onreduced cost of services. This indicates thathospitals operating on economies were betterable to distribute their fixed costs.

2. Hospitals with better qualified staff were able toprovide prompt diagnosis to their patients aswell as reduced consultation and treatment time.

3. Electronic health records (EHR) of patientscontributed to reduced costs of services due

to removal of redundancy and less paperworkinvolved.

4. EHR also allowed hospitals to maintain confi-dentiality of information of their patients ashigh level data would only be accessible tolimited personnel.

5. EHR also reduced the consultation and treat-ment time of patients.

6. Electronic patient scheduling was most influ-ential in providing prompt diagnosis ofdisease as it led to reduced waiting time andfaster diagnosis.

7. Service of doctors had a major impact onalmost all parameters affecting hospital per-formance, except privacy, and secrecy.

Managerial implications

From the analysis conducted, it was found thatquality of healthcare was mainly determined bythree factors namely service of doctors, their compe-tency (ethical, professional, and technical) andcleanliness and that it had a profound impact onthe performance of the hospitals. Hence, managerscould lay more emphasis on selecting doctors withbetter academic qualifications and/or proven workexperience in their field of practice. Also, cleanlinessemerged as an important factor which determinedhospital performance. Hence, hygiene, sanitation,etc. are perceived to be an important factor thatcannot be overlooked.

Information technology has been an enabler ofhealthcare of services as seen from various examplesof its implementation in hospitals around the globe.However, due to the high setup costs, costs ofservers and maintenance charges, hospitals withhigher degree IT implementation tend to have highservice rates. This is expected to come down in the

CMIN

Model NPAR CMIN DF PCMIN/DF

Default model 46 206.020 144 0.001 1.431Saturatedmodel

190 0.000 0

Independencemodel

19 502.832 171 0.000 2.941

Baseline Comparisons

ModelNFI

Delta1RFIrho1

IFIDelta2

TLIrho2 CFI

Default model 0.590 0.513 0.827 0.778 0.813Saturatedmodel

1.000 1.000 1.000

Independencemodel

0.000 0.000 0.000 0.000 0.000

RMSEA

Model RMSEALO90

HI90 PCLOSE

Default model 0.096 0.064 0.124 0.013Independencemodel

0.203 0.183 0.224 0.000

Modification IndicesRegression Weights: (Group number 1 - Default model)

M.I. Par change

Red_conslt_trt_time <--- Red_time_results 5.426 0.276Red_time_results <--- Red_conslt_trt_time 4.779 0.279Privacy_secrecy <--- Promt_diag 4.149 −0.282Privacy_secrecy <--- Diag_equip 4.927 −0.245Food_ser_rate_courtesy <--- Supply_medicine 4.237 0.215Service_of_Doctors <--- Food_courtsey 4.186 −0.159Service_of_Doctors <--- Food_ser_rate_courtesy 4.186 −0.159Cleanliness <--- Red_time_results 4.304 −0.173CDSS <--- Red_conslt_trt_time 4.069 −0.263CDSS <--- Supply_medicine 4.538 0.203Supply_medicine <--- Food_courtsey 8.238 0.397Supply_medicine <--- Food_ser_rate_courtesy 8.238 0.397Supply_medicine <--- CDSS 6.440 0.361Diag_equip <--- Privacy_secrecy 4.971 −0.271



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future with increased usage and lower hardwareand software costs. Overall, hospitals with EHRand CDSS were able to provide prompt diagnosiswith reduced consultation and treatment time.Gradual implementation of same would become anecessity in the future to remain competitive.

Future scope

This paper evaluates the impact of various factors onparameters that determine the performance of hos-pitals in India using structural equation modellingin AMOS using SPSS. However, the study wasrestricted to a small number of hospitals in Tier 2

Covariances: (Group number 1 - Default model)

M.I. Par change

e12 <--> e13 5.158 0.177e10 <--> e11 12.267 0.140e7 <--> e10 6.143 0.127e6 <--> e11 5.011 0.087e6 <--> e10 7.265 0.125e6 <--> e7 6.220 0.122e1 <--> e10 4.815 0.150e1 <--> e2 4.814 0.163e16 <--> e17 5.720 −0.077

Variances: (Group number 1 - Default model)


Infra 0.310 0.124 2.493 0.013IT 0.278 0.088 3.174 0.002Quality 0.237 0.074 3.215 0.001Food_courtsey 0.625 0.129 4.848 ***e13 0.000e1 0.355 0.086 4.139 ***e2 0.225 0.070 3.196 0.001e3 0.277 0.074 3.736 ***e4 0.421 0.104 4.038 ***e5 0.502 0.119 4.231 ***e6 0.117 0.047 2.500 0.012e7 0.267 0.060 4.461 ***e8 0.329 0.081 4.048 ***e9 0.256 0.057 4.519 ***e10 0.127 0.034 3.708 ***e11 0.157 0.037 4.207 ***e12 0.294 0.066 4.439 ***e14 0.184 0.062 2.984 0.003e15 0.429 0.094 4.584 ***e16 0.223 0.051 4.335 ***e17 0.361 0.077 4.673 ***e18 0.345 0.075 4.602 ***e19 0.301 0.067 4.505 ***

Covariances: (Group number 1 - Default model)

M.I. Par change

e12 <--> e13 5.158 0.177e10 <--> e11 12.267 0.140e7 <--> e10 6.143 0.127e6 <--> e11 5.011 0.087e6 <--> e10 7.265 0.125e6 <--> e7 6.220 0.122e1 <--> e10 4.815 0.150e1 <--> e2 4.814 0.163e16 <--> e17 5.720 −0.077



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cities which may not be able to reflect the results inmetros. Comprehensive data need to be collectedfrom rural areas and need to be analysed for thesame. Also, global, macro-economic variables, andother regional biases also tend to have an impacton the performance of the hospitals. They haveincorporated as external error variables in the simu-lation model but their impact cannot be correctlyquantified. Further research can be carried out to

formulate a model to incorporate the same in theanalysis.

Questionnaire

After questionnaire preparation the questionnairewas reviewed several times to make it appropriatefor the targeted people. Please find below thequestionnaire:



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Disclaimer statement

Contributors All the authors have contributed andall the contributors are named as authors.

Funding None.

Conflict of interest No conflict of interest.

Ethics approval The manuscript is compliant toethics guidelines.

ORCID

Sanjay Mohapatra http://orcid.org/0000-0001-9514-862X

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improving patient care in hospital in india by monitoring influential parameters

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