Tech Advisor: Electronic Prescribing Systems with Computer Decision Support

Dr. Luo is assistant clinical professor in the Department of Psychiatry and Biobehavioral Sciences at the University of California in Los Angeles.

 

Electronic prescribing (eRx) has been considered to be the savior for medication errors by creating a printed and legible prescription or an electronically transmitted one. With this innovation, the days of receiving phone calls from the pharmacy asking for clarification of whether the prescription says “Lamictal” or “Lamisil” will hopefully come to an end. However, the benefit of eRx is more than legible prescriptions and drug interaction checks. Decision support systems (DSS) provide much more information, such as whether the patient has another prescription in that drug class; appropriate renal, liver, or age dosing; and formulary availability. This column highlights the impact of DSS in the implementation of eRx systems.

Background

The eRx concept fits right in with the framework of the electronic medical record (EMR) and computer physician order entry system. When first introduced, eRx did not produce the anticipated benefit of reduced pharmacist workload with less data entry; pharmacists still had to modify electronic prescriptions.1 For example, a prescription entered with divalproex sodium 500 mg I PO BID would be modified to divalproex sodium 250 mg II PO BID if the divalproex sodium 500 mg was not available. Time was shifted away from interacting with patients only to focus on fixing prescription problems.

However, Gandhi and colleagues2 reported that in comparison of handwritten prescriptions and basic eRx, computer prescribing systems had significantly less medication error rates but did not have reduced adverse drug events. This apparent lack of improvement with eRx is due to the inability of eRx to make changes that merely enter on the computer what was written on paper. eRx needs to consider human factors and implement electronic information exchanges among clinical systems in order to be successful.3 When these developments are in place, there are many tangible benefits in patient care.

Benefits

Computer-based decision support with guidelines and laboratory data access helped improve decision quality of antibiotic prescribing systems in a study by Sintchenko and colleagues.4 The study used simulated cases treating ventilator-associated pneumonia. Physicians received no additional information, antibiotic guidelines, laboratory reports, or a DSS with laboratory information. The study found that DSS with laboratory information improved the quality of the antibiotic prescription by increasing agreement with an expert panel from 65% to 97%.

Galanter and colleagues5 demonstrated that automated clinical decision support via alerts were able to decrease the ordering and administration of drugs due to renal insufficiency. This study used alerts in a computer physician order entry system that provided a warning about renal insufficiency, the current creatinine clearance, and the recommended safe creatinine clearance. The system also provided a contact number so that the physician could call the on-call pharmacist to discuss this alert if necessary. Decision support at the point of care via personal digital assistants (PDAs) have demonstrated improvement in antibiotic use and decreased length of stay.6 Sintchenko and colleagues6 compared prescribing habits 6 months prior to implementation of the handheld system and 6 months immediately afterwards; the latter time period generated significantly less use of broad spectrum antibiotics and a drop in length of stay, from an average of 7.1 days to an average of 6.2 days per patient. Berner and colleagues7 found that fewer unsafe treatment decisions were made with a PDA-based DSS with a prediction rule for non-steroidal anti-inflammatory drug-related gastrointestinal risk assessment and treatment recommendations. Despite the potential for eRx with decision support to improve quality of health care, there are some barriers to address.

Challenges

A variety of factors have limited the level of success of eRx systems today. Several years ago, handheld eRx appeared to be on the cusp of becoming the standard of practice. There were many vendors developing innovative solutions to transform a Palm or Windows Mobile operating system PDA into an eRx pad. However, it is significant to note that ScanRx, ePhysician, and Parkstone are no longer in business today, and iScribe has been transformed after its purchase by a pharmacy benefit management company. The downfall of handheld prescribing did not parallel the demise of the PDA due to increasing use of the hybrid PDA/phone (also known as the smartphone), but rather is related to the failure of these systems to exchange information back and forth so to produce the goal of saving time and reducing adverse events.

According to Bell and colleagues,8 an eRx system needs to have 14 functional capacities in order to achieve success, which include patient selection, medication selection menus, safety alerts, formulary alerts, dosage calculations, medication administration aids, patient education material, data transmission, alerts for patient failure to refill, diagnosis selection, in-office dispensing, refill and renewal reminders, corollary orders, and automated questionnaires. The ability of the eRx solution to fulfill these goals depends on how well they are integrated into clinical practice. Wang and colleagues9 reviewed 28 commercially available eRx systems according to above recommendations and determined that these systems met only 50% of the criteria. This assessment is an important issue because while many academic medical centers may have the healthcare information technology resources to develop their own eRx solution, community hospitals may not. Integration of eRx in an EMR system met 60% of the criteria, compared to only 35% for stand-alone eRx systems.

Despite the benefits of an electronic prescription system, they are still often plagued by poor physician acceptance.10-13 If a clinical DSS in the eRx system sends too many alerts and misses important alerts, physicians may refuse to use the application. Too many alerts of low clinical significance will lead to high override rates and the potential to override important alerts.14 The developers of Athena DSS highlight that a system should not only improve quality of care but must also focus on patient safety since automating systems can potentially create new and unforeseen errors.15 They identified nine potential sources of errors in the human-computer interaction, which included potential harms due to medication withdrawal; missing data leading to recommendation of a contraindicated drug; potential interaction of the recommended drug with another drug prescribed for the patient; inaccuracies in program inputs or program logic which could lead to erroneous recommendations; potential harm due to rearranging clinician priorities with required use of this DSS; knowledge gaps of the clinician-user that are directly relevant to the DSS recommendations; generating false expectations on the part of the clinician-user that the system will alert them to all problems; potential for data overload; and potential for incorrect recommendations on cases that the system was not designed to handle. Saleem and colleagues16 analyzed the factors that impacted physician adherence with reminder systems, and discovered that environmental factors played a significant role in adoption or rejection of guidelines. Impediments to reminder use included lack of coordination between nurses and providers; using the reminders while not with the patient, thus impairing data acquisition and/or implementation of recommended actions; workload; lack of clinical recommendation flexibility; and poor interface usability. In addition to the physician-computer interface and physician adoption problems, one of the primary hurdles is the development of standards for the prescribing and dispensing of medications.

Steps Toward Improvement

Each potential barrier identified above needs to be addressed in eRx system development in order to improve quality, usability, and efficacy. Alerts that have been typically overridden due to poor specificity and alert overload can be modified and tailored for the ambulatory care setting in order to minimize interruptions in workflow by designating only critical and high-severity alerts to interrupt clinician workflow.17 Saleem and colleagues16 identified environmental factors that facilitated adoption of guideline utilization, including limiting the number of reminders at a site; strategic location of the computer workstations; integration of reminders into workflow; and the ability to document system problems and receive prompt administrator feedback. Information at the electronic point of entry for antimicrobial prescribing would be most likely to be used and would improve antimicrobial prescribing.18 The developers of the MOXXI PDA eRx system found the printed prescriptions, current drug list, and re-prescribing functions as the most beneficial aspects of the system, which drove its use by physicians.19 These physicians were more likely to use the system to identify the drug profile for patients who used more medication, made more emergency department visits, had more prescribing physicians, and had lower continuity of care. Organizational issues are also an important element in having technical success in implementing DSS.20 The developers of the ATHENA DSS used a ‘‘sociotechnical’’ approach to integrate the system into primary care clinics. They applied an iterative technical design method in response to organizational input and obtained ongoing endorsements of the project by the organization’s administrative and clinical leadership. Miller and colleagues3 call for a national standard for drug interaction information format as well as a “terminology system for prescribing.” In the report of the Joint Clinical Decision Support Workgroup of the American Medical Informatics Association, Teich and colleagues21 describe an outline of development and desiderata for CDS eRx systems in terms of advances in system capabilities, uniform standards and vocabularies, and incentives to promote adoption.

Conclusion

eRx alone has been demonstrated to not have limited clinical adoption, which has been reflected in how that segment of the healthcare information technology industry has struggled in the last several years. Integration with EMRs along with DSS have demonstrated improved clinical outcomes, but eRx continues to be a work in development. It is part of the multi-faceted and agency process toward improving quality of patient care and reducing medication errors and adverse outcomes. Deciding when to jump in and implement an eRx system is a complicated decision, requiring a commitment of technical support, finances, and foresight into whether a home-grown or off-the-shelf system will continue to be a work in progress. However, despite these limitations, the benefits are tangible and real to patients today.

References

1. Murray MD, Loos B, Tu W, Eckert GJ, Zhou XH, Tierney WM. Effects of computer-based prescribing on pharmacist work patterns. J Am Med Inform Assoc. 1998;5(6):546-553.

2. Gandhi TK, Weingart SN, Seger AC, et al. Impact of basic computerized prescribing on outpatient medication errors and adverse drug events. J Am Med Inform Assoc. 2002;9(suppl):S48-S49.

3. Miller RA, Gardner RM, Johnson KB, Hripcsak G. Clinical decision support and electronic prescribing systems: a time for responsible thought and action. J Am Med Inform Assoc. 2005;12(4):403-409.

4. Sintchenko V, Coiera E, Iredell JR, Gilbert GL. Comparative impact of guidelines, clinical data, and decision support on prescribing decisions: an interactive web experiment with simulated cases. J Am Med Inform Assoc. 2004;11(1):71-77.

5. Galanter WL, Didomenico RJ, Polikaitis A. A trial of automated decision support alerts for contraindicated medications using computer physician order entry. J Am Med Inform Assoc. 2005;12(3):269-274.

6. Sintchenko V, Iredell JR, Gilbert GL, Coiera E. Handheld computer-based decision support reduces patient length of stay and antibiotic prescribing in critical care. J Am Med Inform Assoc. 2005;12(4):398–402.

7. Berner ES, Houston TK, Ray MN, et al. Improving ambulatory prescribing safety with a handheld decision support system: a randomized controlled trial. J Am Med Inform Assoc. 2006;13(2):171-179.

8. Bell DS, Cretin S, Marken RS, Landman AB. A conceptual framework for evaluating outpatient electronic prescribing systems based on their functional capabilities. J Am Med Inform Assoc. 2004;11(1):60-70.

9. Wang CJ, Marken RS, Meili RC, Straus JB, Landman AB, Bell DS. Functional characteristics of commercial ambulatory electronic prescribing systems: a field study. J Am Med Inform Assoc. 2005;12(3):346-356.

10. Berg M. Implementing information systems in health care organizations: myths and challenges. Int J Med Inf. 2001;64(2-3):143-156.

11. Berg M. Patient care information systems and health care work: a sociotechnical approach. Int J Med Inf. 1999;55(2):87-101.

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14. Payne TH, Nichol WP, Hoey P, Savarino J. Characteristics and override rates of order checks in a practitioner order entry system. Proc AMIA Symp. 2002;602-606.

15. Goldstein MK, Hoffman BB, Coleman RW, et al. Patient safety in guideline-based decision support for hypertension management: ATHENA DSS. J Am Med Inform Assoc. 2002;9(suppl):S11-S16.

16. Saleem JJ, Patterson ES, Militello L, Render ML, Orshansky G, Asch SM. Exploring barriers and facilitators to the use of computerized clinical reminders. J Am Med Inform Assoc. 2005;12(4):438-447.

17. Shah NR, Seger AC, Seger DL, et al. Improving acceptance of computerized prescribing alerts in ambulatory care. J Am Med Inform Assoc. 2006;13(1):5-11.

18. Sellman JS, Decarolis D, Schullo-Feulner A, Nelson DB, Filice GA. Information resources used in antimicrobial prescribing. J Am Med Inform Assoc. 2004;11(4):281-284.

19. Tamblyn R, Huang A, Kawasumi Y, et al. The development and evaluation of an integrated electronic prescribing and drug management system for primary care. J Am Med Inform Assoc. 2006;13(2):148-159.

20. Goldstein MK, Coleman RW, Tu SW, et al. Translating research into practice: organizational issues in implementing automated decision support for hypertension in three medical centers. J Am Med Inform Assoc. 2004;11(5):368-376.

21. Teich JM, Osheroff JA, Pifer EA, Sittig DF, Jenders RA, The CDS Expert Review Panel. Clinical decision support in electronic prescribing: recommendations and an action plan: report of the joint clinical decision support workgroup. J Am Med Inform Assoc. 2005;12(4):365-376.