Published on in Vol 5 , No 2 (2017) :Apr-Jun

Applying STOPP Guidelines in Primary Care Through Electronic Medical Record Decision Support: Randomized Control Trial Highlighting the Importance of Data Quality

Applying STOPP Guidelines in Primary Care Through Electronic Medical Record Decision Support: Randomized Control Trial Highlighting the Importance of Data Quality

Applying STOPP Guidelines in Primary Care Through Electronic Medical Record Decision Support: Randomized Control Trial Highlighting the Importance of Data Quality

Journals

  1. Rogero-Blanco E, Lopez-Rodriguez J, Sanz-Cuesta T, Aza-Pascual-Salcedo M, Bujalance-Zafra M, Cura-Gonzalez I. Use of an Electronic Clinical Decision Support System in Primary Care to Assess Inappropriate Polypharmacy in Young Seniors With Multimorbidity: Observational, Descriptive, Cross-Sectional Study. JMIR Medical Informatics 2020;8(3):e14130 View
  2. Bloomfield H, Greer N, Linsky A, Bolduc J, Naidl T, Vardeny O, MacDonald R, McKenzie L, Wilt T. Deprescribing for Community-Dwelling Older Adults: a Systematic Review and Meta-analysis. Journal of General Internal Medicine 2020;35(11):3323 View
  3. Zhang Y, Koru G. Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions. Journal of the American Medical Informatics Association 2020;27(3):386 View
  4. Mangin D, Lawson J, Adamczyk K, Guenter D. Embedding “Smart” Disease Coding Within Routine Electronic Medical Record Workflow: Prospective Single-Arm Trial. JMIR Medical Informatics 2020;8(7):e16764 View
  5. Monteiro L, Maricoto T, Solha I, Ribeiro-Vaz I, Martins C, Monteiro-Soares M. Reducing Potentially Inappropriate Prescriptions for Older Patients Using Computerized Decision Support Tools: Systematic Review. Journal of Medical Internet Research 2019;21(11):e15385 View
  6. Scott I, Pillans P, Barras M, Morris C. Using EMR-enabled computerized decision support systems to reduce prescribing of potentially inappropriate medications: a narrative review. Therapeutic Advances in Drug Safety 2018;9(9):559 View
  7. Doocy S, Paik K, Lyles E, Hei Tam H, Fahed Z, Winkler E, Kontunen K, Mkanna A, Burnham G. Guidelines and mHealth to Improve Quality of Hypertension and Type 2 Diabetes Care for Vulnerable Populations in Lebanon: Longitudinal Cohort Study. JMIR mHealth and uHealth 2017;5(10):e158 View
  8. Earl T, Katapodis N, Schneiderman S, Shoemaker-Hunt S. Using Deprescribing Practices and the Screening Tool of Older Persons’ Potentially Inappropriate Prescriptions Criteria to Reduce Harm and Preventable Adverse Drug Events in Older Adults. Journal of Patient Safety 2020;16(3):S23 View
  9. Meid A, Groll A, Heider D, Mächler S, Adler J, Günster C, König H, Haefeli W. Prediction of Drug-Related Risks Using Clinical Context Information in Longitudinal Claims Data. Value in Health 2018;21(12):1390 View
  10. Anfinogenova N, Trubacheva I, Popov S, Efimova E, Ussov W. Trends and concerns of potentially inappropriate medication use in patients with cardiovascular diseases. Expert Opinion on Drug Safety 2021:1 View
  11. Shojania K, Jennings A, Ramsay C, Grimshaw J, Kwan J, Lo L. The effects of on-screen, point of care computer reminders on processes and outcomes of care. Cochrane Database of Systematic Reviews 2009;2021(6) View