Informatics in Primary Care now full-free text and free to publish

Simon de Lusignan

Editor in Chief, Informatics in Primary Care

Professor of Primary Care and Clinical Informatics, University of Surrey


This is the first issue of the journal in its new full-free-text, fully open access format; this issue will also appear in print. The generosity of BCS, The Chartered Institute for IT, means that Informatics in Primary Care is now free for authors to publish in, and free for anyone to read. The journal has moved to being published online by BCS. This is a return to the journal’s roots, as between 1995 and 2000 the journal was published by the BCS Primary Health Care Specialist Group (PHCSG). All the back numbers of the journal are now also open access (Table 1), either on the IngentaConnect™ website (44 issues from between 2001 and 2012) and with the very early issues available on the University of Nottingham website (1995–2000).

Table 1


This issue contains two papers about prescribing. One reports how satisfaction with a prescribing prompts system ScriptSwitch® was reduced because it did not learn prescribers’ preferences, so the same pop-ups had to be repeatedly deleted.1 This unhelpful signal to noise ratio is something previously reported within the pages of this journal.2 Preference learning is a long established element of computing, and particular machine learning.3 It is disappointing that dissatisfaction arose in an area where there is a technology available to correct it. A second paper in this issue describes a web-based application to share prescribing data between general practitioner, pharmacist and hospital was broadly welcomed by those involved in its use.4


A simple measure has been created to assess health related quality of life, called ‘howRU.’ This quick to answer four element tool5 is compared with the EuroQual Group’s measure EQ-5D originally called Euroqol, a widely respected measure of health-related quality of life.6


An important paper by Zhou et al. suggests that interoperable computerised medical record systems primarily reduce administrative task time such as prescribing, rather than improving overall efficiency of consulting or reduce patient waiting.7 These findings are compatible with a classic systematic review from over a decade ago,8 and your Editor’s findings in direct observation of computerised consultations.9 A short report suggests that getting on with your computerised medical record is associated with job satisfaction.10 With computerised medical records forming such an integral part of clinical practice it is perhaps no surprise. Perhaps it is time for aptitude and utilisation of computing to form part of the assessment of trainers and trainees for general practice?11


A systematic review suggests that use of the computer in the consulting room promotes more systematic collection of biomedical data, possibly at the expense of the biopsychosocial.12 A commentary reminds us that this is a ‘triadic’ relationship13 involving doctor-patient and computer.14


Visualisation of data is set to rise in health care. This can be at an individual patient level15 through the use of geographical information systems (GIS) to look at population trends or changes.16 This issue contains an interesting paper from India about the use of GIS for malaria mapping.17


1. Hire C and Rushford B. General practitioners’ views on using a prescribing substitution application (ScriptSwitch®). Informatics in Primary Care 2013;21(1):1–11.

2. Vaziri A, Connor E, Shepherd I, Jones RT, Chan T and de Lusignan S. Are we setting about improving the safety of computerised prescribing in the right way? A workshop report. Informatics in Primary Care 2009;17(3):175–82.

3. Fürnkranz J and Hüllermeier E. Preference Learning. Berlin: Springer-Verlag, 2010.

4. Geurts MME, Ivens M, van Gelder E and de Gier JJ. Development of a web-based pharmaceutical care plan to facilitate collaboration between healthcare providers and patients. Informatics in Primary Care 2013;21(1):53–9.

5. Benson T, Potts HWW, Whatling JM and Patterson D. Comparison of howRU and EQ-5D measures of health-related quality of life in an outpatient clinic. Informatics in Primary Care 2013;21(1):12–7.

6. Brazier J, Jones N and Kind P. Testing the validity of the Euroqol and comparing it with the SF-36 health survey questionnaire. Quality of Life Research 1993;2(3):169–80.

7. Zhou Y, Ancker JS, Upahdye M, McGeorge NM, Guarrera TK, Hedge S et al. The impact of interoperability of electronic health records on ambulatory physician practices: a discrete-event simulation study. Informatics in Primary Care 2013;21(1):21–9.

8. Mitchell E and Sullivan F. A descriptive feast but an evaluative famine: systematic review of published articles on primary care computing during 1980–97. BMJ 2001;322(7281):279–82.

9. Kumarapeli P and de Lusignan S. Using the computer in the clinical consultation; setting the stage, reviewing, recording, and taking actions: multi-channel video study. Journal of the American Medical Informatics Association 2013 Jun;20(e1):e67–75. doi: 10.1136/amiajnl-2012-001081.

10. Jones CD, Holmes GM, Lewis SE, Thompson KW, Cykert S and DeWalt DA. Satisfaction with electronic health records is associated with job satisfaction among primary care physicians. Informatics in Primary Care 2013;21(1):18–20.

11. de Lusignan S, Pearce C, Munro N, Getting on with your computer is associated with job satisfaction in primary care: entrants to primary care should be assessed for their competency with electronic patient record systems. Inform Prim Care. 2013;21(1):i–iii.

12. Kazmi Z. Effects of exam room EHR use on doctor–patient communication. Informatics in Primary Care 2013;21(1):30–39.

13. Scott D and Purves I. Triadic relationship between doctor, computer and patient. Interacting with Computers 1996;8(4):347–63.

14. Pearce CM, Kumarapeli P and de Lusignan S. Commentary: Effects of exam room EHR use on doctor–patient communication: a systematic literature review—triadic and other key terms may have identified additional literature. Informatics in Primary Care 2013;21(1)40–42.

15. Poh N and de Lusignan S. Data-modelling and visualisation in chronic kidney disease (CKD): a step towards personalised medicine. Informatics in Primary Care 2011;19(2):57–63.

16. Cole J, Colledge M, Megaw T, Powls M, Bullock S and Keen J. The implementation of electronic services: planned or organic growth? Informatics in Primary Care 2005;13(3):187–93.

17. Rai PK, Nathawat MS and Rai S. Using the information value method in a geographic information system and remote sensing for malaria mapping. Informatics in Primary Care 2013;21(1):43–52.


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