Development and efficacy of a computerized decision support system for osteoporosis management in the community

Inbal Goldshtein, Orna Shamai-Lubovitz, Michal Guindy, Varda Shalev, Gabriel Chodick, Uri Lerner, Vanessa Rouach

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Summary: Computerized alerts for primary care physicians, provided during visits of patients who met treatment guidelines based on their electronic medical records, are an efficient method to raise awareness to many otherwise missed cases, especially after fracture. Purpose: Measure the efficacy of an automated real-time alert which was developed to assist osteoporosis management in the community. Methods: The study population included treatment naïve patients with T-score ≤ − 2.5 or hip or vertebral fracture in a 2 million member Israeli health fund. On each ambulatory visit to a primary care physician or endocrinologist, a pop-up screen reminded the caregiver to consider treatment initiation. A follow-up “smart-set” screen conveniently gathered links to common actions (namely, (a) issue first line therapy prescription, (b) referral to nutritionist consultation, (c) laboratory tests relevant for osteoporosis, and (d) printing an information page for the patient). Time till treatment initiation was compared between the 3 years prior to and following the intervention. Results: Within 2 years since alert activation, a total of n = 21,070 cases were alerted, 52% of which were long standing cases: untreated for over 6 months since the event. During this period, a total of 30% initiated treatment purchases. As compared with the 3 years prior to the intervention, time till treatment initiation decreased following the intervention with HR = 1.05, 1.94, 1.29 (p values = 0.020, < 0.001, 0.005) for T-score, hip, and vertebral cases respectively. Initiation rates within 6 months increased from 52.0 to 59.8%, from 12.3 to 27.7%, and from 17.4 to 27.1% among T-score, hip, and vertebral cases, respectively (p value < 0.001). Male sex, nursing home residence, having diabetes or a cardiovascular disease and age younger than 60 or older than 80 were associated with lower treatment rates. Conclusions: A computerized decision support system can efficiently raise attention to many otherwise missed high-risk osteoporotic cases, particularly those after fractures.

Original languageEnglish
Article number27
JournalArchives of Osteoporosis
Issue number1
StatePublished - 1 Dec 2020
Externally publishedYes


  • Alert
  • Osteoporosis

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine


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