Qualitative analysis of multi-disciplinary round-table discussions on the acceleration of benefits and data analytics through hospital electronic prescribing (ePrescribing) systems

Kathrin Cresswell, Jamie Coleman, Pam Smith, Charles Swainson, Ann Slee, Aziz Sheikh


Background: Electronic systems that facilitate prescribing, administration and dispensing of medicines (ePrescribing systems) are at the heart of international efforts to improve the safety, quality and efficiency of medicine management. Considering the initial costs of procuring and maintaining ePrescribing systems, there is a need to better understand how to accelerate and maximise the financial benefits associated with these systems.

Objectives: We sought to investigate how different sectors are approaching the realisation of returns on investment from ePrescribing systems in U.K. hospitals and what lessons can be learned for future developments and implementation strategies within healthcare settings.

Methods: We conducted international, multi-disciplinary, round-table discussions with 21 participants from different backgrounds including policy makers, healthcare organisations, academic researchers, vendors and patient representatives. The discussions were audio-recorded, transcribed and then thematically analysed with the qualitative analysis software NVivo10.

Results: There was an over-riding concern that realising financial returns from ePrescribing systems was challenging. The underlying reasons included substantial fixed costs of care provision, the difficulties in radically changing the medicines management process and the lack of capacity within NHS hospitals to analyse and exploit the digital data being generated. Any future data strategy should take into account the need to collect and analyse local and national data (i.e. within and across hospitals), setting comparators to measure progress (i.e. baseline measurements) and clear standards guiding data management so that data are comparable across settings.

Conclusions: A more coherent national approach to realising financial benefits from ePrescribing systems is needed as implementations progress and the range of tools to collect information will lead to exponential data growth. The move towards more sophisticated closed-loop systems that integrate prescribing, administration and dispensing, as well as increasingly empowered patients accessing their data through portals and portable devices, will accelerate these developments. Meaningful analysis of data will be the key to realise benefits associated with systems.


ePrescribing, data analytics, returns on investment

Full Text:



Blumenthal D and Tavenner M. The “Meaningful Use” regulation for electronic health records. The New England Journal of Medicine 2010;363:501–504. http://dx.doi.org/10.1056/NEJMp1006114. PMid:20647183.

NHS England. The Integrated Digital Care Technology Fund. Available from: http://www.england.nhs.uk/ourwork/tsd/sst/tech-fund/. Accessed on 05 May 2015.

Cresswell K, Coleman J and Slee A et al. Investigating and learning lessons from early experiences of implementing ePrescribing systems into NHS hospitals: a questionnaire study. Public Library of Science One 2013;8:e53369.

The Office of the National Coordinator for Health Information Technology Office of the Secretary. Update on the adoption of health information technology and related efforts to facilitate the electronic use and exchange of health information. The Office of the National Coordinator for Health Information Technology Office of the Secretary, U.S. Department of Health and Human Services. Available from: http://www.healthit.gov/sites/default/files/rtc_adoption_and_exchange9302014.pdf. Accessed on 05 May 2015.

Black A, Car J and Pagliari C, Anandan C, Cresswell K and Bokun T et al. The Impact of eHealth on the quality and safety of health care: a systematic overview. Public Library of Science Medicine 2011;8:e1000387.

Lilford RJ, Chilton PJ, Hemming K, Girling AJ, Taylor CA and Barach P. Evaluating policy and service interventions: framework to guide selection and interpretation of study end points. British Medical Journal 2010;341:c4413.

Bergmo TS. Can economic evaluation in telemedicine be trusted? A systematic review of the literature. Cost Effectiveness and Resource Allocation 2010;24:7–18.

Field TS, Rochon P, Lee M, Gavendo L, Subramanian S and Hoover S et al. Costs associated with developing and implementing a computerized clinical decision support system for medication dosing for patients with renal insufficiency in the long-term care setting. Journal of the American Medical Informatics 2008;15:466. http://dx.doi.org/10.1197/jamia.m2589.

The Guardian. NHS deficit could hit £2.5bn this year, warns top health chief. Available from: http://www.theguardian.com/society/2015/mar/19/nhs-deficit-crisis-bailout-warns-top-health-chief. Accessed on 05 May 2015.

American Banker. What’s the ROI of mobile banking? 15.7%, Forrester says. Available from: http://www.americanbanker.com/bulletins/-1037534-1.html. Accessed on 05 May 2015.

Gartner. Gartner survey reveals that 64 percent of organizations have invested or plan to invest in Big Data in 2013. Available from: http://www.gartner.com/newsroom/id/2593815. Accessed on 05 May 2015.

Gartner. Available from: http://www.gartner.com/technology/home.jsp. Accessed on 05 May 2015.

Kaiser Permanente. Available from: https://healthy.kaiserpermanente.org/html/kaiser/index.shtml. Accessed on 05 May 2015.

Geisinger Health System. Available from: http://www.geisinger.org. Accessed on 05 May 2015.

University Hospitals Birmingham. Quality and Outcomes Research Unit. Available from: http://www.uhb.nhs.uk/quoru.htm. Accessed on 05 May 2015.

Chaudhry B, Wang J, Wu S, Maglione M, Mojica W and Roth E et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Annals of Internal Medicine 2006;144:742–752. http://dx.doi.org/10.7326/0003-4819-144-10-200605160-00125. PMid:16702590.

Kitzinger J. Qualitative research: introducing focus groups. British Medical Journal 1995;311:299–302. http://dx.doi.org/10.1136/bmj.311.7000.299. PMid:7633241 PMCid:PMC2550365.

Patton M. Qualitative Research. John Wiley & Sons, Ltd: New Jersey, United States, 2005.

QSR International. NVivo 10 for Windows. Available from: http://www.qsrinternational.com/products_nvivo.aspx. Accessed on 05 May 2015.

Pope C, Ziebland S and Mays N. Qualitative research in health care: analysing qualitative data. British Medical Journal 2000;320(7227):114. http://dx.doi.org/10.1136/bmj.320.7227.114. PMid:10625273 PMCid:PMC1117368.

Edwards PN, Bowker GC, Jackson SJ and Williams R. Introduction: an agenda for infrastructure studies. Special Issue on e-Infrastructure of the Journal of the Association for Information Systems 2009;10:364–374.

Creswell JW and Miller DL. Determining validity in qualitative inquiry. Theory into practice 2000;39:124-130. http://dx.doi.org/10.1207/s15430421tip3903_2.

Franklin BD, O’Grady K, Donyai P, Jacklin A and Barber N. The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study. Quality and Safety in Health Care 2007;16:279–284. http://dx.doi.org/10.1136/qshc.2006.019497. PMid:17693676 PMCid:PMC2464943.

HIMSS Analytics. Available from: http://himssanalytics.org/provider-solutions. Accessed on 05 May 2015.

Cresswell K, Mozaffar H, Shah S and Sheikh A. A systematic assessment of approaches to promoting the appropriate use of antibiotics through hospital electronic prescribing systems. International Journal of Pharmacy Practice (in press).

Bennett CC and Hauser K. Artificial intelligence framework for simulating clinical decision making: a markov decision process approach. Artificial Intelligence in Medicine 2013;57:9–19. http://dx.doi.org/10.1016/j.artmed.2012.12.003. PMid:23287490.

EHR Impact. Interoperable eHealth is worth it. Available from: http://www.ehr-impact.eu/downloads/documents/ehr_impact_study_final.pdf. Accessed on 05 May 2015.

Cresswell K, Bates DW and Sheikh A. Ten Key Considerations for the Successful Optimization of Large-scale Health Information Technology. Journal of the American Medical Informatics Association (in press).

Kaushal R, Jha AK, Franz C, Glaser J, Shetty KD and Jaggi T et al. Return on investment for a computerized physician order entry system. Journal of the American Medical Informatics 2006;13:261–266. http://dx.doi.org/10.1197/jamia.M1984. PMid:16501178 PMCid:PMC1513660.

Kuperman G and Gibson RF. Computer physician order entry: benefits, costs, and issues. Annals of Internal Medicine 2003;139:31–39. http://dx.doi.org/10.7326/0003-4819-139-1-200307010-00010. PMid:12834316.

Ure J, Procter R, Lin Y, Hartswood M, Anderson S, Lloyd S et al. The development of data infrastructures for ehealth: a socio-technical perspective. Journal of the Association for Information Systems 2009;10(5):3

DOI: http://dx.doi.org/10.14236/jhi.v23i2.178


  • There are currently no refbacks.

This is an open access journal, which means that all content is freely available without charge to the user or their institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal starting from Volume 21 without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open accessFor permission regarding papers published in previous volumes, please contact us.

Privacy statement: The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party.

Online ISSN 2058-4563 - Print ISSN 2058-4555. Published by BCS, The Chartered Institute for IT