Big data in pharmacy practice: current use, challenges, and the future

Pharmacy informatics is defined as the use and integration of data, information, knowledge, technology, and automation in the medication-use process for the purpose of improving health outcomes. The term “big data” has been coined and is often defined in three V’s: volume, velocity, and variety. This paper describes three major areas in which pharmacy utilizes big data, including: 1) informed decision making (clinical pathways and clinical practice guidelines); 2) improved care delivery in health care settings such as hospitals and community pharmacy practice settings; and 3) quality performance measurement for the Centers for Medicare and Medicaid and medication management activities such as tracking medication adherence and medication reconciliation.

[1]  B. Zarowitz,et al.  Critical Pathways: The Time Is Here for Pharmacist Involvement , 1996, Pharmacotherapy.

[2]  John F Hurdle,et al.  High rates of adverse drug events in a highly computerized hospital. , 2005, Archives of internal medicine.

[3]  F. Marra,et al.  Antibiotic use among children in British Columbia, Canada. , 2006, The Journal of antimicrobial chemotherapy.

[4]  C. Sprung,et al.  Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock 2012 , 2013, Critical care medicine.

[5]  H. S. Lau,et al.  The completeness of medication histories in hospital medical records of patients admitted to general internal medicine wards. , 2000, British journal of clinical pharmacology.

[6]  C. Nightingale,et al.  How to develop critical paths and prepare for other formulary management changes. , 1994, Hospital formulary.

[7]  ELSKE AMMENWERTH,et al.  Review Paper: The Effect of Electronic Prescribing on Medication Errors and Adverse Drug Events: A Systematic Review , 2008, J. Am. Medical Informatics Assoc..

[8]  Stephen Wilson Data protection: Big data held to privacy laws, too , 2015, Nature.

[9]  Christian Lovis,et al.  Securing Chemotherapies: Fabrication, Prescription, Administration and Complete Traceability , 2007, MedInfo.

[10]  E. Etchells,et al.  Frequency, type and clinical importance of medication history errors at admission to hospital: a systematic review , 2005, Canadian Medical Association Journal.

[11]  D. Bates,et al.  The Incidence and Severity of Adverse Events Affecting Patients after Discharge from the Hospital , 2003, Annals of Internal Medicine.

[12]  M. Levy,et al.  Surviving Sepsis Campaign: International guidelines for management of severe sepsis and septic shock: 2008 , 2007, Intensive Care Medicine.

[13]  R. Resar,et al.  Standardization as a mechanism to improve safety in health care. , 2004, Joint Commission journal on quality and safety.

[14]  Jyotishman Pathak,et al.  Empowering personalized medicine with big data and semantic web technology: Promises, challenges, and use cases , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[15]  Sung-joon Min,et al.  Posthospital medication discrepancies: prevalence and contributing factors. , 2005, Archives of internal medicine.

[16]  Catherine L. Liang,et al.  Effect of an electronic medication reconciliation application and process redesign on potential adverse drug events: a cluster-randomized trial. , 2009, Archives of internal medicine.

[17]  Peter Groves,et al.  The 'big data' revolution in healthcare: Accelerating value and innovation , 2016 .

[18]  Jianhua Li,et al.  Medication Reconciliation Using Natural Language Processing and Controlled Terminologies , 2007, MedInfo.

[19]  Tejal K. Gandhi,et al.  Design and implementation of an application and associated services to support interdisciplinary medication reconciliation efforts at an integrated healthcare delivery network. , 2006, Journal of the American Medical Informatics Association : JAMIA.

[20]  T. Brennan,et al.  Abusive prescribing of controlled substances--a pharmacy view. , 2013, The New England journal of medicine.

[21]  M. Levy,et al.  Surviving Sepsis Campaign: International guidelines for management of severe sepsis and septic shock: 2008 , 2007, Intensive Care Medicine.

[22]  I. Silver Privacy and the First Amendment , 1966 .

[23]  Dotan A. Haim,et al.  Using Networks to Combine “Big Data” and Traditional Surveillance to Improve Influenza Predictions , 2015, Scientific Reports.

[24]  Michael J. Paul,et al.  Twitter Improves Influenza Forecasting , 2014, PLoS currents.

[25]  M. Etminan,et al.  Long-term persistence with orlistat and sibutramine in a population-based cohort , 2007, International Journal of Obesity.

[26]  R. Evans,et al.  Deriving Measures of Intensive Care Unit Antimicrobial Use from Computerized Pharmacy Data: Methods, Validation, and Overcoming Barriers , 2011, Infection Control & Hospital Epidemiology.

[27]  J. Jollis,et al.  Medication errors in hospitalized cardiovascular patients. , 2003, Archives of internal medicine.

[28]  B. Zarowitz,et al.  Critical Pathways: The Role of Pharmacy Today and Tomorrow , 2006, Pharmacotherapy.

[29]  L. Tanoue,et al.  The National Comprehensive Cancer Network , 1998, Cancer.

[30]  Using medication reconciliation to prevent errors. , 2006, Sentinel event alert.

[31]  Robert Nguyen,et al.  Use of the Refill Function Through an Online Patient Portal is Associated With Improved Adherence to Statins in an Integrated Health System , 2013, Medical care.

[32]  J. Drazen,et al.  Prescriptions, privacy, and the First Amendment. , 2011, The New England journal of medicine.

[33]  R M Gardner,et al.  Computer surveillance of hospital-acquired infections and antibiotic use. , 1986, JAMA.