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How can e-prescribing and clinical decision support in secondary care be best utilised to improve patient care?

31 August 2009

As published on www.bjhcim.co.uk, August 2009

Neill Jones of First DataBank Europe highlights the major factors for ensuring that technology for e-prescribing and clinical decision support is implemented in a way that supports improvements in the provision of healthcare, by streamlining and integrating data sources, engaging staff in the change process and providing the appropriate tools to improve workflow and patient outcomes.

Experience of electronic prescribing in secondary care in the UK and the US is evidence that the appropriate application of IT (including e-prescribing and clinical decision support (CDS) with real-time alerting) can reduce the risk of medical error and support the improvement of patient care.

But e-prescribing and CDS can only be effective in improving patient care to its full potential with a multidisciplinary approach and proactive leadership to drive positive change. So how can healthcare leaders ensure that these tools are used to their full effect?  And how can CDS and e-prescribing be made to work effectively?

Successful application

Technology’s role in assisting decision making in healthcare is ever evolving. CDS has repeatedly demonstrated its worth when evaluated. The claims fall into three broad categories: improved patient safety; improved quality of care; and improved efficiencies in healthcare delivery[1].

In order for e-prescribing and CDS applications to be utilised successfully, however, they must be implemented well, with thorough system integration prioritised. Equally, staff must be willing and able to ensure supportive technology can reach its full potential in streamlining processes and reducing risk.

A successful implementation of e-prescribing and CDS should facilitate and support change, creating an environment in which healthcare professionals can transform the benefits provided by technology into tangible improvements to patient care and organisational efficiencies.

Healthcare organisations implementing e-prescribing and CDS technology must understand what class of decision support their e-prescribing systems can support, ensure that the clinical knowledge underlying the CDS is reasonable and must represent individual patient data appropriately, to enhance the CDS. These factors will determine to what extent an institution will succeed with its e-prescribing implementation and achieve its set targets [2].

Streamlining data sources

The impact of CDS increases as more types of data and workflow are combined together in a single system or interoperable set of systems [3]. Increasingly, time and effort is being expended in secondary care to ensure that medicines reconciliation occurs for patients as soon as possible following admission to hospital.

This requires an up-to-date record of the drugs that the patient is taking or should be taking. This can be a very time-consuming process that requires information from a number of different sources and systems where a patient record is held (both electronic and paper-based); from the GP system, previous hospital records, emergency department, nursing homes records and the patient.

Furthermore, an otherwise excellent e-prescribing and CDS system that contains incorrect, ambiguous or incomplete patient information will produce suboptimal results. After 'passing' simulation testing, extensive clinical testing (involving real patients) should occur in carefully monitored settings [4].

Customisation

Information-system users most value systems that deliver information at the time that it is needed and guide users by offering alternatives, rather than simply stopping them from doing something. The use and value of the system should be monitored to improve the alerts and identify areas for further training.

The clinical significance of active alerts, such as drug interactions, drug duplications or contraindications, at the point of prescribing, need to be interpreted for individual patients [5]. By capturing the reasons for any overrides at the point of use, further analysis will reveal if the override was justified on an individual patient basis or whether further improvements or customisation of the CDS are required.

In addition, it will flag up user training needs. By researching where and when clinicians accept alerts or where they need to be customised, more of a balance between over and under-alerting can be established. Having the flexibility to manage the threshold for alerting is critical to deriving the most benefit from CDS; too low and the clinician is overwhelmed with alerts [6]; too high and safety benefits are eroded[7]. Additional work is required to explore the optimal alerting for each different user and care setting combination.

Integration into clinician workflow

The benefits of an IT system may differ across different human settings of work; the application of any given computerised system in the UK may be different from its effectiveness in the US [8]. In order to derive the most benefit from CDS it must be provided automatically as part of the normal clinician workflow and at the time and place of decision making [9].

When clinicians have to actively search for decision-support tools and then enter (or re-enter) the clinical data required to generate output, the utility and efficiency, as well as the use of decision support, decrease [10]. As most prescribers do not know that they have made an error, it follows that software must run constantly in the background to intercept slips and lapses.

Engaging staff

The effectiveness of CDS depends not just on the way it handles patient data, but also on who uses it and under what conditions [11]. Users need to understand and participate in implementation and development. In addition, clinical leaders need to engage users in the process, to enthuse their team and ensure that users across the board buy into the benefits of using these technologies.

The amount of training required should not be underestimated and should go on well beyond the implementation phase and become a permanent fixture, given the turnover of staff and system developments that will ensue.
Electronic CDS should never be designed to replace human knowledge and judgement but to provide up-to-date information to support clinicians in their own decision making.

Where such systems are already in use, users are required to have a solid working knowledge about what is, and is not, available and receive regular training and support in order to optimise the benefits for patients.
Developing an electronic patient record as a means of accessing and sharing patient data across the NHS will be a gradual process. Wholly digital healthcare may be an aspiration but there are real patients, with real health problems who need to be cared for in the meantime.

Electronic CDS is available now and is already providing invaluable support to clinicians. By optimising its use and increasing its distribution, healthcare organisations can enhance the quality of care and improve patient safety even further.

Case study

After the introduction of e-prescribing and CDS on a four-ward medical unit at Montagu Hospital, Doncaster and Bassetlaw Hospitals NHS Foundation Trust, compliance with trust policy for recording of drug administration rose from 65% to 100% and potential adverse drug events (events with the potential to cause harm, delay recovery or result in a lack of control of symptoms) were reduced by over 61% [12].

Neill Jones, Clinical Director, First DataBank Europe

References

1. Sintchenko V, Westbrook J, Tipper S, et al. Electronic Decision Support Activities in Different Healthcare Settings in Australia. Appendix A in: National Electronic Decision Support Taskforce. Electronic Decision Support for Australia's Health Sector. Canberra, Department of Health and Aging, November 2002. www.health.gov.au/internet/hconnect/publishing.nsf/Content/
7746B10691FA666CCA257128007B7EAF/$File/nedsrept.pdf 
Accessed 18 August 2009.
2. Kuperman GJ, Bobb A, Payne TH, et al. Medication-related clinical decision support in computerized provider order entry systems: A review. J Am Med Inform Assoc 2007; 14: 29-40.
3. Teich JM, Osheroff JA, Pifer EA, et al. Clinical decision support in electronic prescribing; Recommendations and an action plan. J AM Med Inform Assoc 2005; 12:365-376.
4. Miller RA, Gardner RM, Johnson KB, et al. Clinical decision support and electronic prescribing systems: A time for responsible thought and action. J Am Med Inform Assoc 2005; 12: 403-409.
5. Slee A, Farrar K, Hughes D, et al. Electronic prescribing – implications for hospital pharmacy. Hospital Pharmacist 2007; 14:217–220.
6. Weingart SN, Toth M, Sands DZ, et al. Physicians’ Decision to Override. Computerized Drug Alerts in Primary Care. Arch Intern Med 2003; 163:2625-31.
7. Shah NR, Seger AC, Seger DL, et al. Improving acceptance of computerized alerts in ambulatory care. J Am Med Inform Assoc 2006;  13:5-11.
8. Barber N. Designing information technology to support prescribing decision making. Qual and Safety in Health Care 2004; 13:450-454.
9. Kawamoto K, Houlihan CA, Balas EA, et al. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330:765-768.
10. Handler JA, Feied CF, Coonan K, et al.Computerized physician order entry and online decision support. Acad Emerg Med 2004; 11:1135-1141.
11. Barber N. Designing information technology to support prescribing decision making. Qual and Safety in Health Care 2004; 13:450-454.
12. Barker A, Kay J. Electronic Prescribing Improves Patient Safety — An Audit. Pharm J 2007; 14:225.

Contact Information
Katie Sanders, Marketing Executive
01392 440 181
katie_sanders@firstdatabank.co.uk

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