Life In The Slower Lane

Here is the PICO analysis for the article:

Full Article Title:

Development and Pilot Testing of Computerized Order Entry Algorithms for Geriatric Problems in Nursing Homes

Authors: Colón-Emeric CS, Schmader KE, Twersky J, et al.

Journal: Journal of the American Geriatrics Society (JAGS) (2009); 57(9):1644–1653

DOI: 10.1111/j.1532-5415.2009.02387.x

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✅ PICO Analysis

Type of Study:

Pre–post, quasi-experimental pilot study

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P – Population

• Setting:Two Veterans Affairs (VA) nursing homes (community living centres) in the United States
• Participants:
  • 265 randomly selected residents with one or more of five common geriatric conditions: falls, fever, pneumonia, urinary tract infection (UTI), and osteoporosis
  • 42 nursing home providers, including physicians, nurse practitioners (NPs), physician assistants, and trainees
• Eligibility Criteria:
  • Residents with relevant diagnoses or clinical indicators for inclusion per VA system and chart review
  • Excluded if discharged or deceased within 48 hours of acute condition onset or if treatment-limiting advance directives applied

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I – Intervention

• Intervention Type:Implementation of computerized order entry algorithms (COEAs) for five common geriatric problems:
  1. Falls
  2. Fever
  3. Pneumonia
  4. Urinary tract infection
  5. Osteoporosis
• Features of COEAs:
  • Based on clinical practice guidelines and expert panel review
  • Embedded into the VA’s electronic medical record system
  • Single-screen order sets including diagnostic/treatment options and interdisciplinary communication prompts (e.g., automatic pharmacist alert for falls)
  • Promoted via laminated cards, flyers, in-person orientations, and algorithm-specific reminders

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C – Comparison

• Comparator:
  • The same facilities’ data for the 6 months prior to implementation (pre–post design)
  • No control group used
• Baseline Characteristics:
  • Pre- and post-implementation groups were similar across demographics and comorbidity profiles 

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O – Outcomes

Primary Process Outcomes (COEA Use):

• Usage rates by condition:
  • Falls: 73%
  • Fever: 9%
  • Pneumonia: 8%
  • UTI: 7%
  • Osteoporosis: 3%
• Provider acceptance:
  • High satisfaction with usability and time savings reported by the subset of users who engaged consistently

Secondary Outcomes:

Person-Centred Outcomes (Quality Indicators):

• Falls:
  • Improvements seen in 6 of 9 indicators (e.g., increased vitamin D/calcium prescribing, neuroleptic reduction, hip protector use)
  • External hip protector use rose from 25% to 47.5% (p = .06)
  • Sedative-hypnotic reduction improved from 16.7% to 50% (p = .30)
• Other Conditions:
  • No consistent improvement across fever, UTI, pneumonia, or osteoporosis indicators
  • Decline in osteoporosis assessments and treatments may reflect differences in diagnostic coding or resident inclusion across periods

Health System Outcomes:

• Resource utilization:
  • No significant change in lab or radiology testing between periods
  • Provider turnover negatively affected algorithm use in one site but not the other

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Summary Conclusion

This pilot study showed that computerized order entry algorithms (COEAs) were feasible and well-received by staff, but actual usage was highly variable. Most impact was seen in falls-related care, likely due to regulatory focus and higher incidence. For other conditions, use was low, especially where no acute event triggered clinician action. The study highlights the need for better integration of reminders, broader engagement (e.g. nursing staff), and alignment with clinical workflows to realise the full potential of COEAs in aged care .

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