Journal:Use of handheld computers in clinical practice: A systematic review

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Full article title Use of handheld computers in clinical practice: A systematic review
Journal BMC Medical Informatics & Decision Making
Author(s) Mickan, Sharon; Atherton, Helen; Roberts, Nia Wyn; Heneghan, Carl; Tilson, Julie K
Author affiliation(s) Nuffield Department of Primary Care Health Sciences, University of Oxford;
Division of Biokinesiology and Physical Therapy, University of Southern California
Primary contact Email: Sharon.mickan@phc.ox.ac.uk
Year published 2014
Volume and issue 14
Page(s) 56
DOI 10.1186/1472-6947-14-56
ISSN 1472-6947
Distribution license Creative Commons Attribution 2.0
Website http://www.biomedcentral.com/1472-6947/14/56
This article should not be considered complete until this message box has been removed. This is a work in progress.

Abstract

Background

Many healthcare professionals use smartphones and tablets to inform patient care. Contemporary research suggests that handheld computers may support aspects of clinical diagnosis and management. This systematic review was designed to synthesise high quality evidence to answer the question; Does healthcare professionals’ use of handheld computers improve their access to information and support clinical decision making at the point of care?

Methods

A detailed search was conducted using Cochrane, MEDLINE, EMBASE, PsycINFO, Science and Social Science Citation Indices since 2001. Interventions promoting healthcare professionals seeking information or making clinical decisions using handheld computers were included. Classroom learning and the use of laptop computers were excluded. Two authors independently selected studies, assessed quality using the Cochrane Risk of Bias tool and extracted data. High levels of data heterogeneity negated statistical synthesis. Instead, evidence for effectiveness was summarised narratively, according to each study’s aim for assessing the impact of handheld computer use.

Results

We included seven randomised trials investigating medical or nursing staffs’ use of Personal Digital Assistants. Effectiveness was demonstrated across three distinct functions that emerged from the data: accessing information for clinical knowledge, adherence to guidelines and diagnostic decision making. When healthcare professionals used handheld computers to access clinical information, their knowledge improved significantly more than peers who used paper resources. When clinical guideline recommendations were presented on handheld computers, clinicians made significantly safer prescribing decisions and adhered more closely to recommendations than peers using paper resources. Finally, healthcare professionals made significantly more appropriate diagnostic decisions using clinical decision making tools on handheld computers compared to colleagues who did not have access to these tools. For these clinical decisions, the numbers need to test/screen were all less than 11.

Conclusion

Healthcare professionals’ use of handheld computers may improve their information seeking, adherence to guidelines and clinical decision making. Handheld computers can provide real time access to and analysis of clinical information. The integration of clinical decision support systems within handheld computers offers clinicians the highest level of synthesised evidence at the point of care. Future research is needed to replicate these early results and to identify beneficial clinical outcomes.

Keywords: Handheld computers; Smartphone; Information-seeking behaviour; Evidence-based practice; Knowledge translation; Clinical decision support systems; Clinical guidelines; Diagnostic decision making

Background

Increasing numbers of healthcare professionals use handheld computers that offer instant access to vast amounts of information via the internet and healthcare applications (apps).[1] Over the last 10 years there has been a rapid and accelerating rate of innovation in handheld computers, from personal digital assistants (PDAs) towards more powerful, versatile and internet connected devices. As the rate of adoption of handheld computers has increased, individual patterns of usage have moved from that of communication and personal diary management towards information seeking and decision support.[2] Today’s clinicians can use handheld computers to search the internet for evidence and guidance on drugs and clinical conditions, use clinical decision support systems (CDSS) and access highly detailed patient information from clinical and laboratory investigations.

At the same time, there has been a change in the acceptance of using handheld computers in healthcare settings. Now, most students and many professionals are enthusiastic about using smartphones and tablet computers, and they take them wherever they go.[3] Along with this increasing adoption of handheld computers, there has been a massive growth in the volume of synthesized research information, healthcare oriented apps, databases and CDSSs.

This has also sparked an increased production of feasibility research, which has yet to recommend strategies for engagement, efficacy or effectiveness of mobile health initiatives.[4] While both early and current systematic reviews offer tentative and sceptical conclusions, there is equipoise in the literature. A systematic review of the use of PDAs in clinical decision making reported an increase in data collection quality and concluded that the use of decision support software improved the appropriateness of diagnostic and treatment decisions.[2] In a broader and contemporary systematic review of mHealth technologies, modest benefits were reported for improved clinical diagnosis and management support, and mixed outcomes were reported for efficient and accurate documentation.[3] Further, there was no clear benefit for educational interventions and some evidence of reduced quality of clinical assessment, when using mobile technology based photos.

When healthcare professionals communicate with patients, there is high quality evidence to support the use of mobile phones to transmit short message service (SMS) reminders to improve attendance at health care appointments.[5][6] Further, text messaging interventions were shown to increase adherence to antiretroviral therapy in low-income settings and increased smoking cessation in high income settings.[7]

An early review of computerised, rather than mobile, CDSSs for prescribing, described effectiveness in initiating and monitoring therapy, but provided little evidence on their impact in specific clinical settings.[8] A later review reported improved processes of care in 60% of included studies but improved patient outcomes in only 20% of studies.[9] It is not clear whether incorporating these computerised systems into mobile devices would produce similar results.

A literature and commercial review of mobile CDSSs reported medical professionals using a growing number of apps across a wide range of fields.[10] A systematic review of smartphone healthcare apps identified seven functional categories in which apps have been developed for use by healthcare professionals: diagnosis, drug reference, medical calculators, literature search, clinical communication, access to hospital information systems, and medical training.[1] A scoping review from a further five systematic reviews concluded that there is evidence for effective use of handheld computers by healthcare professionals across four key functions: providing easy and timely access to information, enabling accurate and complete documentation, providing instant access to evidence-based decision support and patient management systems, and promoting efficient work practices.[11]

Most published studies to date describe the design, development and implementation of handheld computers using observational study designs.[4] In order to determine the benefits of integrating handheld computer use in healthcare practice, it is important to summarise and quantify results from the highest quality randomised controlled trials (RCTs) of effectiveness studies. Based on the functions identified in the earlier scoping review[11], it is timely to better understand whether healthcare professionals’ use of handheld computers facilitates information seeking and improved clinical decision making. The purpose of this review is to answer the research question “Does healthcare professionals’ use of handheld computers improve their access to information and support clinical decision making at the point of care?”.

Methods

The protocol for this systematic review was registered with PROSPERO (CRD42011001632), updated and adhered to. http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42011001632#.U7-vibFnDhA

Search strategy

We searched the following databases from 2001 to 19th August 2013: Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, PsycINFO, Science Citation Index and Social Science Citation Index. The MEDLINE search strategy can be found in the Additional file 1. Reference lists of included studies were hand searched.

Additional file 1. Medline search strategy.
Format: DOC Size: 39KB Download file

This file can be viewed with: Microsoft Word Viewer

Inclusion and exclusion criteria

We included studies whose participants were healthcare professionals using handheld devices in clinical settings. Interventions of interest were those investigating the use of handheld computers to promote healthcare professionals’ information seeking (outside of formal education courses), or to support informed clinical decision making. Our comparator was usual clinical practice. We excluded the use of laptops.

Study designs included were RCTs. The review was restricted to the English language. We searched from 2001 onwards to account for the changing nature of technology. We excluded studies that were presented as abstracts only, and where author contact confirmed the study had not been published in full.

Study selection

Two authors (SM and JT) screened titles and abstracts. Full text articles were obtained for those selected and screened for inclusion (SM and HA). Where necessary, authors of studies were contacted for clarification of inclusion status.

Data extraction

A data extraction form was designed and piloted by two authors (SM and HA) to record study design, country, device used, aim, participants, setting, intervention, comparator, primary and secondary outcome data (as reported by the systematic review authors). The same authors independently extracted data. Disagreements were resolved by discussion.

Assessment of quality

Assessment of risk of bias was conducted at the study level using the Cochrane Risk of Bias tool.[12] Assessment was conducted independently by two authors (SM and HA) with disagreements resolved by discussion. Information on risk of bias status was used to aid interpretation of the included studies.

Data synthesis

High levels of data heterogeneity and mixed data quality meant that statistical synthesis was not possible. We adopted a narrative approach to summarise the evidence for effectiveness according to the purpose for using the handheld computer.

Results

The combined search strategies identified 5,888 titles. After duplicates were removed, 3,612 titles were screened for eligibility. Thirty-eight full text articles were read, of which 31 did not meet the inclusion and exclusion criteria and therefore seven studies were retained for data extraction (see Figure 1).

Fig1 Mickan BMCMedInfoDecMak2014 14.jpg

Figure 1. Flow diagram

Characteristics of the seven included studies are summarised in Table 1. All were RCTs, mostly designed as pilot studies with comparatively small numbers of participants (range 12-76 participants). Although we intended to include studies investigating smartphones and tablets, to represent the most current forms of handheld computers, all included studies investigated the use of PDAs. Three studies were conducted in USA, two in Canada and one each in France and Australia. In five studies, the intervention group used a PDA while the control group used paper-based resources. In two studies, both groups used a PDA, but the intervention group had access to a specific clinical decision support system (CDSS) or information tool that the control group did not. Healthcare participants were either medical (residents, fellows, and family, general and emergency physicians) or nursing professionals. Where students were included, they were using a PDA in a clinical environment.


Table 1: Characteristics of included studies
Study, country Participants, setting Intervention Comparator Primary outcome Secondary outcome
Berner 2006 USA[13] 59 Internal medicine residents, University outpatient clinic PDA with rule for gastrointestinal risk assessment when prescribing NSAIDS PDA without rule for gastrointestinal risk assessment when prescribing NSAIDS Difference in unsafe NSAID prescriptions Identification of key risk factors for standardised patient case
Bochicchio 2006 USA[14] 12 1st year critical care fellows, University hospital PDA with John Hopkins Antibiotic Guide No PDA, instructed to use written reference guides Difference in mean score for knowledge test Antibiotic decision accuracy
Farrell 2008 Australia[15] 76 nursing students, Medical-surgical wards PDA with pharmacological information and training session No training or PDA Difference in mean score for pharmacology test N/A
Price 2005 Canada[16] 8 General practitioners, General practice (79 patients) PDA with reminder for 5 preventive measures Software provided after the study Adherence to five guidelines N/A
Roy 2009 France[17] 24 Emergency physicians, 10 emergency departments (1645 patients) PDA with CDSS for pulmonary embolism PDA used for data collection only; Paper based guideline material Appropriate diagnostic strategy for pulmonary embolism Adherence to recommended diagnostic testing Number of tests per patient
Greiver 2005 Canada[18] 18 Family physicians, Family practice (65 patients) PDA with angina diagnosis software Conventional care Appropriate referral for cardiac stress testing at presentation, and nuclear cardiology after cardiac stress testing Referral to cardiologists
Lee 2009 USA[19] 29 registered nurses, Hospital and ambulatory care (1874 patients) PDA with CDSS for obesity diagnosis PDA without CDSS for obesity diagnosis Appropriate obesity related diagnosis Missed obesity related diagnosis

References

  1. ↑ 1.0 1.1 Mosa, A.S.; Yoo, I.; Sheets, L. (2012). "A systematic review of healthcare applications for smartphones". BMC Medical Informatics & Decision Making 12: 67. doi:10.1186/1472-6947-12-67. PMID 22781312. 
  2. ↑ 2.0 2.1 Divall, P.; Camosso-Stefinovic, J.; Baker, R. (2013). "The use of personal digital assistants in clinical decision making by health care professionals: a systematic review". Health Informatics Journal 19 (1): 16-28. doi:10.1177/1460458212446761. PMID 23486823. 
  3. ↑ 3.0 3.1 Free, C.; Phillips, G.; Watson, L.; Galli, L.; Felix, L.; Edwards, P.; Patel, V.; Haines, A. (2013). "The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis". PLoS Medicine 10 (1): e1001363. doi:10.1371/journal.pmed.1001363. PMC PMC3566926. PMID 23458994. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3566926. 
  4. ↑ 4.0 4.1 Tomlinson, M.; Rotheram-Borus, M.J.; Swartz, L.; Tsai, A.C. (2013). "Scaling up mHealth: where is the evidence?". PLoS Medicine 10 (2): e1001382. doi:10.1371/journal.pmed.1001382. PMC PMC3570540. PMID 23424286. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3570540. 
  5. ↑ Car, J.; Gurol-Urganci, I.; de Jongh, T.; Vodopivec-Jamsek, V.; Atun, R. (2012). "Mobile phone messaging reminders for attendance at healthcare appointments". Cochrane Database of Systematic Reviews 7: Cd007458. doi:10.1002/14651858.CD007458. PMID 22786507. 
  6. ↑ Guy, R.; Hocking, J.; Wand, H.; Stott, S.; Ali, H.; Kaldor, J. (2012). "How effective are short message service reminders at increasing clinic attendance? a meta-analysis and systematic review". Health Services Research 47 (2): 614-632. doi:10.1111/j.1475-6773.2011.01342.x. PMC PMC3419880. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3419880. 
  7. ↑ Free, C.; Phillips, G.; Galli, L.; Watson, L.; Felix, L.; Edwards, P.; Patel, V.; Haines, A. (2013). "The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review". PloS Medicine 10 (1): e1001362. doi:10.1371/journal.pmed.1001362. PMC PMC3548655. PMID 23349621. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3548655. 
  8. ↑ Pearson, S-A.; Moxey, A.; Robertson, J.; Hains, I.; Williamson, M.; Reeve, J.; Newby, D. (2009). "Do computerised clinical decision support systems for prescribing change practice? A systematic review of the literature (1990-2007)". BMC Health Services Research 9 (1): 154. doi:10.1186/1472-6963-9-154. PMC PMC2744674. PMID 19715591. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2744674. 
  9. ↑ Nieuwlaat, R.; Connolly, S.; Mackay, J.; Weise-Kelly, L.; Navarro, T.; Wilczynski, N.; Brian Haynes, R.; Team tCSR (2011). "Computerized clinical decision support systems for therapeutic drug monitoring and dosing: a decision-maker-researcher partnership systematic review". Implementation Science 6 (1): 90. doi:10.1186/1748-5908-6-90. PMC PMC3170236. PMID 21824384. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3170236. 
  10. ↑ Martínez-Pérez, B.; de la Torre-Díez, I.; López-Coronado, M.; Sainz-de-Abajo, B.; Robles, M.; García-Gómez, J.M. (2014). "Mobile clinical decision support systems and applications: a literature and commercial review". Journal of Medical Systems 38 (1): 4. doi:10.1007/s10916-013-0004-y. PMID 24399281. 
  11. ↑ 11.0 11.1 Mickan, S.; Tilson, K.J.; Atherton, H.; Roberts, W.N.; Heneghan, C. (2013). "Evidence of effectiveness of health care professionals using handheld computers: a scoping review of systematic reviews". Journal of Medical Internet Research 15 (10): e212. doi:10.2196/jmir.2530. PMC PMC3841346. PMID 24165786. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3841346. 
  12. ↑ Higgins, J.P.; Altman, D.G.; Tzsche, P.C.; Ni, P.; Moher, D.; Oxman, A.D.; Savovic, J.; Schulz, K.F.; Weeks, L.; Sterne, J.A.C. (2011). "The Cochrane Collaboration's tool for assessing risk of bias in randomised trials". BMJ 343: d5928. doi:10.1136/bmj.d5928. PMC PMC3196245. PMID 22008217. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3196245. 
  13. ↑ Berner, E.S.; Houston, T.K.; Ray, M.N.; Allison, J.J.; Heudebert, G.R.; Chatham, W.W.; Kennedy, J.I.; Glandon, G.L.; Norton, P.A.; Crawford, M.A.; Maisiak, R.S. (2006). "Improving ambulatory prescribing safety with a handheld decision support system: a randomized controlled trial". Journal of the American Medical Informatics Association 13 (2): 171-179. doi:10.1197/jamia.M1961. PMC PMC1447547. PMID 16357350. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447547. 
  14. ↑ Bochicchio, G.V.; Smit, P.A.; Moore, R.; Bochicchio, K.; Auwaerter, P.; Johnson, S.B.; Scalea, T.; Bartlett, J.G. (2006). "Pilot study of a web-based antibiotic decision management guide". Journal of the American College of Surgeons 202 (3): 459-467. doi:10.1016/j.jamcollsurg.2005.11.010. PMID 16500251. 
  15. ↑ Farrell, M.J.; Rose, L. (2008). "Use of mobile handheld computers in clinical nursing education". Journal of Nursing Education 47 (1): 13-19. PMID 18232610. 
  16. ↑ Price, M. (2005). "Can hand-held computers improve adherence to guidelines? A (Palm) Pilot study of family doctors in British Columbia". Canadian Family Physician 51 (11): 1506-1507. PMC PMC1479487. PMID 16926943. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1479487. 
  17. ↑ Roy, P-M.; Durieux, P.; Gillaizeau, F.; Legall, C.; Armand-Perroux, A.; Martino, L.; Hachelaf, M.; Dubart, A-E.; Schmidt, J.; Cristiano, M.; Jean-Marie, C.; Arnaud, P.; Guy, M. (2009). "A computerized handheld decision-support system to improve pulmonary embolism diagnosis: a randomized trial". Annals of Internal Medicine 151 (10): 677-686. doi:10.7326/0003-4819-151-10-200911170-00003. PMID 19920268. 
  18. ↑ Greiver, M.; Drummond, N.; White, D.; Weshler, J.; Moineddin, R.; Network NTPCR (2005). "Angina on the Palm: randomized controlled pilot trial of Palm PDA software for referrals for cardiac testing". Canadian Family Physician 51 (3): 382-383. PMC PMC1472967. PMID 16926933. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1472967. 
  19. ↑ Lee, N.J.; Chen, E.S.; Currie, L.M.; Donovan, M.; Hall, E.K.; Jia, H.; John, R.M.; Bakken, S. (2009). "The effect of a mobile clinical decision support system on the diagnosis of obesity and overweight in acute and primary care encounters". Advances in Nursing Science 32 (3): 211-221. doi:10.1097/ANS.0b013e3181b0d6bf. PMC PMC1472967. PMID 19707090. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1472967. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In most of the article's references DOIs and PubMed IDs were not given; they've been added to make the references more useful.

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