Information need assessment of health care workers in large hospitals of Delhi: an empirical study
The state of hospitals both it is public or private after implementation and smooth running of healthcare information systems will be transformed in all aspects. It will further provide benefits to all health care workers (HCW) by timely fulfilling the information needs. It is anticipated that the implementation will have better results and will decrease medical errors, escalate legibility, reduce costs and enhance the quality of healthcare system. Moreover, if the need of HCW is identified and served properly only then it increases the effectiveness of health care information system (HCIS). The issues regarding standardization of information needs will be maintained, if the management of electronic medical records, technology acceptability, resistance, adoption, implementation, policy issues and security are taken care with utmost caution. This paper identifies the IT interventions used in obtaining needful information of HCW through computing confirmatory factor analysis on the data collected from heath care workers of large hospitals of Delhi.
KeywordsHealth care information system (HCIS) Health care workers (HCW) Confirmatory factor analysis (CFA) Health care information technology (HIT)
We are heartily grateful to all the public and private hospitals authorities for giving permission to conduct the study. Moreover, we are also thankful to all the hospital health care workers that they gave time and understood the importance of the study. Many of them had helped a lot in conducting the research by making us reach the right respondent. We are grateful to all the authors for doing research earlier, so that we were able to read and refer their research for a better understanding. We should not forget to give special thanks to our family because without their motivation, we could not have walked a single step.
- 2.Government of Western Australia Department of Health. Health care worker. www.health.wa.gov.au/circularsnew/pdfs/12891.pdf. Accessed 12 Jan 2017
- 8.Khandhar M, Singh A (2008) Health management information system. In: Gupta P, Bagga RK (eds) Compendium of E-governance: initiatives in India, vol 2. Universities Press, Hyderbad, pp 248–256Google Scholar
- 9.Jagirdhar K (2011) Srishti Software—Jayadeva Hospital HMIS—case study. http://blogs.siliconindia.com/. Accessed 15 Jan 2017
- 11.Hammid F, Cline TW (2013) Providers’ acceptance factors and their perceived barriers to electronic health record (EHR) adoption. Online J Nurs Inf 17 (3)Google Scholar
- 16.Williams RM, Baker LM, Marshall JG (1992) Information Searching in Health Care. SLACK Inc, ThorofareGoogle Scholar
- 17.Nunnally JC (1967) Psychometric theory. Tata McGraw-Hill, New YorkGoogle Scholar
- 19.Diamantopoulos A, Reynolds N, Schlegelmilch BB (1994) Pre-testing in questionnaire design: the impact of respondent characteristics on error detection. J Mark Res Soc 36(4):295–313Google Scholar
- 20.Delhi Govt (2016) List of hospitals. http://www.delhi.gov.in/wps/wcm/connect/DoIT_Health/health/home/. Accessed 15 Jan 2017
- 21.Williams B, Brown T, Onsman A (2010) Exploratory factor analysis: a five-step guide for novices. Australas J Paramed 8(3). http://ro.ecu.edu.au/jephc/vol8/iss3/1. Accessed 18 Jan 2017
- 25.Joreskog K, Sorbom D (1993) LISREL 8 structural equation modeling with the SIMPLIS command language. Scientific Software International, Chicago, p 302Google Scholar
- 28.Carlson, Herdman (2012) www.management.pamplin.vt.edu/directory/Articles/Carlson1.pdf. Accessed 21 Jan 2017
- 29.Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL (2009) Multivariate data analysis. Prentice Hall, New DelhiGoogle Scholar