Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2020 May 15.
Published in final edited form as: Health Informatics J. 2018 Dec 10;26(1):172–180. doi: 10.1177/1460458218813640

Use of electronic health record data from diverse primary care practices to identify and characterize patients prescribed common medications

Allison M Cole 1, Kari A Stepehens 2, Imara West 3, Gina A Keppel 4, Ken Thummel 5, Laura-Mae Baldwin 6
PMCID: PMC6557686  NIHMSID: NIHMS1002232  PMID: 30526246

Abstract

Objectives:

We use prescription of statin medications and prescription of warfarin to explore the capacity of electronic health record (EHR) data to: 1) describe cohorts of patients prescribed these medications, and 2) identify cohorts of patients with evidence of adverse events related to prescription of these medications.

Methods:

This study was conducted in the Washington, Wyoming, Alaska, Montana and Idaho (WWAMI) region Practice and Research Network using Data QUEST, an electronic data-sharing infrastructure. We used EHR data to describe cohorts of patients prescribed statin or warfarin medications and reported the proportions of patients with adverse events.

Results:

Among the 35,445 active patients, 1,745 received at least one statin prescription and 301 received at least one warfarin prescription. Only 3% of statin patients had evidence of myopathy. 51 patients (17% of those prescribed warfarin) had a bleeding complication.

Conclusions:

Primary care EHR data can effectively be used to identify patients prescribed specific medications and patients potentially experiencing medication adverse events.

Introduction

Electronic health record (EHR) data are increasingly used for research, though much of the evidence is from research conducted using EHR data from large health systems. EHRs are designed for billing, patient care documentation, and communications (e.g., e-prescribing) (Bayley 2013, Coorevits 2013). Potential challenges to conducting research with EHR data are well documented, and include assessing and accounting for missing data, inconsistencies in data entry among clinical providers, and inability to easily obtain data stored in free text (Bayley 2013). Despite these limitations, use of EHR data for research offers unprecedented opportunity to address critical scientific questions that may be otherwise difficult to answer using claims data or traditional clinical research (Ragupathi 2014). Medication adverse events is a field especially well suited for research with EHR data (Jensen 2012). Study of medication adverse events often requires large numbers of potential subjects and the ability to identify relatively rare patients who might be experiencing adverse events. Primary care settings, the majority of which use EHR systems, are optimal for understanding epidemiology and clinical implications of medication adverse events, because of the prevalence of prescription medication use in primary care settings and continuity of care permitting longitudinal observations. EHR data from primary care systems can also allow inclusion of research priority populations, such as rural residents, who may not participate in medical research conducted exclusively in academic health centers. EHRs typically capture medication data because physicians routinely order and transmit prescriptions to pharmacies electronically (www.healthit.gov). EHRs additionally capture patient demographics, clinical laboratory, and diagnosis information linked to prescription data. In contrast, claims, or billing data have limitations which may create significant barriers for research, such as exclusion of patients without insurance and inability to link prescriptions to clinical data such as diagnoses or laboratory results (Pladeval 2004, Crystal 2007).

We use EHR data from primary care practices to explore two key classes of medications, both of which are commonly prescribed in primary care and have associated potential adverse events – statins and warfarin. Statins are commonly prescribed medications that are effective in treating hyperlipidemia and reducing cardiovascular disease risk (DeWilde 2003, Ward 2007, Trialists 2008, Taylor 2013, Bibbons-Domingo 2016). Statin-induced myopathy is an adverse drug-related condition that includes myalgia, myositis, and rhabdomyolysis due to use of statin medications (Sathisivam 2008). The incidence of statin-induced myopathy reported in clinical trials ranges from 0.44 to 5.34 per 10,000 person years (Gasit 2001, Graham 2004).. Warfarin is an effective anticoagulant that reduces the risk of thromboembolism in patients with atrial fibrillation (Go 2003, Reynolds 2004). Treatment with warfarin requires regular blood test monitoring and dosage adjustment to achieve adequate therapeutic response and minimize risk of complications. We focused on warfarin, rather than newer oral anticoagulants, which where not in widespread during the time period covered by the EHR data extract. Overtreatment with warfarin may lead to adverse events due to excessive bleeding risk, such as hemorrhagic stroke or gastrointestinal hemorrhage.

The rationale for this study is to explore the capacity of EHR data within a network of community-based primary care practices to achieve three objectives: 1) identification and description of cohorts of patients prescribed statin or warfarin medications, 2) characterization of the types of prescription information available in the EHR data, and 3) identification of cohorts of patients prescribed statins or warfarin with evidence of adverse events relevant to these medications. Given the widespread availability of EHR data for research purposes, further description of methods for conducting research with this data as well as elucidation of the limitations are needed. The findings from this study provide approaches for future clinical researchers studying medication adverse events, potentially supporting observational clinical research or recruitment of patients from primary care settings.

Methods

Setting and Data Source

This study was conducted in the WWAMI region Practice and Research Network (WPRN), a network of more than 60 primary care practices across Washington, Wyoming, Alaska, Montana and Idaho. At the time of this study, a subset of 9 clinics participated in Data Query Extraction Standardization Translation (Data QUEST), an infrastructure for sharing outpatient primary care EHR data for research (Stephens 2012). Data QUEST practices represent diverse primary care practices, including federally qualified health centers and rural-serving primary care practices. Data QUEST includes patient level data stored securely within local practices’ firewalls in aligned data repositories and uses a federated data-sharing infrastructure to support regulation-compliant governance of data between primary care partners and researchers (Stephens 2012, Stephens 2016). Data QUEST’s infrastructure and governance maintain compliance with Health Insurance Portability and Accountability Act (HIPAA) and Institutional Review Board (IRB) regulations. Data QUEST targets extraction from main data domains in the EHRs, including demographics, vital signs, diagnosis codes, diagnostic test results, social and family history, prescriptions, and physical examination findings.

For this project, we worked with two clinical organizations from the WPRN that participate in Data QUEST. These organizations were selected based on data availability at the time of the research. The two organizations represent six clinics. Each clinical organization is an independent entity, with distinct instances of Centricity™ EHR systems. The Human Subjects Review Board at the University of Washington reviewed and determined this study to be exempt.

Time Period

This study required continuous EHR data over a 2-year period so that we could both identify patients prescribed a medication and have sufficient observation time to capture potential adverse events. For organization A the observation period we used was from 12/1/2010–11/30/2012, and for organization B, the observation period we used was from 9/1/2012–8/31/2014. The time periods were selected based on the most recent two-year period during which each organization had continuously available EHR data. Organization A had 6 years of EHR data (7/1/2006–11/30/2012) and organization B had 5 years (7/1/2009–8/31/2014) of EHR data.

Measures

Active Patients.

We defined active patients as those with either a blood pressure recorded by the clinic or a Current Procedural Terminology (CPT) code for an in-person primary care office visit at any time during the two-year study period noted above. Only patients who were ages 18 or older on the first day of the study period were included.

Demographics.

Demographics included patients’ gender, age, and race/ethnicity (American Indian/Alaska Native, Asian, Black or African American, Caucasian, Hispanic or Latino, Multiracial, Native Hawaiian or Pacific Islander or No information). Race/ethnicity data was combined into a single field in both EHR systems that contributed data for this study.

Prescribed Medications.

Using EHR-prescribed medications data, we identified patients with at least one prescription ordered for either a statin medication or warfarin during the study period. We created string-based search terms that included all brand and generic names for the medication groups (warfarin and statins) to identify relevant medication prescription data. We used a wild card search strategy to identify complete or partial medication names within the medication fields that would be indicative of either statin or warfarin use. Coded medication data, such as national drug code (NDC) was not available in either EHR system at the time of this study.

Statin or Warfarin-related Medication Adverse Events.

For inclusion as a statin or warfarin-related adverse event, the adverse event had to occur after the first prescription in the two-year study period for the selected medication. Adverse events were selected based on clinical relevance and feasibility of measurement using outpatient EHR data. For detection of selected laboratory abnormalities, we used published reference ranges when available (Oake 2008), or calculated the mean value in our dataset, and designated three times the mean value as the cut off for abnormal. Among patients prescribed a statin medication, we included the following adverse events: 1) a diagnosis of myopathy or statin myopathy using appropriate ICD-9 codes (Appendix A), and 2) laboratory evidence of elevated creatine kinase (creatine kinase ≥ 486 units, based on 3x dataset mean), elevated aspartate amino transferase (AST) (AST ≥ 78, based on 3x dataset mean), or elevated alanine amino transferase (ALT) (ALT ≥ 87, based on 3x dataset mean). Among patients prescribed warfarin, we included the following adverse events: 1) a bleeding complication using an ICD-9 code (Appendix A), 2) laboratory evidence of elevated international normalized ration (INR) (INR>5, Oake 2008), and 3) receipt of vitamin K, based on evidence of a vitamin K medication electronic order.

Results

We counted the number of prescriptions per patient for both the statin and warfarin cohorts and reported the mean number of prescriptions per patient. To characterize the types of prescription information available in the dataset, we calculated the proportion of prescriptions that contained structured data reporting the dose, unit (e.g. milligrams), quantity (e.g. thirty), signetur instructions (sig) or numbers of refills. For all patients in each of the medication cohorts, we calculated the proportions with evidence of medication-related adverse events.

Objective 1: Identification and Description of Patients Prescribed Statin or Warfarin Medications

The dataset included 35,445 active, adult patients. Table 1 reports overall and individual organization counts of patients on statin (n = 1,745, 4.9% of patients seen during the time period) or warfarin (n = 301, 0.8%) medications.

Table 1:

Description of adults taking either statin medications or warfarin across two primary care organizations

Characteristic Adults with a Statin Medication Adults with a Warfarin Medication
Overall Organization A Organization B Overall Organization A Organization B
Number of Adults, n 1,745 403 1,342 301 106 195
Age, n (%)
18 – 44 226 (13%) 42 (10%) 184 (14%) 54 (18%) 19 (18%) 35 (18%)
45 – 64 962 (55%) 196 (49%) 766 (57%) 135 (45%) 40 (38%) 95 (49%)
65 and older 557 (32%) 165 (41%) 392 (29%) 112 (37%) 47 (44%) 65 (33%)
Gender, n (%)
Female 971 (56%) 223 (55%) 748 (56%) 144 (48%) 57 (54%) 87 (45%)
Male 774 (44%) 180 (45%) 594 (44%) 157 (52%) 49 (46%) 108 (55%)
Race/Ethnicity, n (%)
American Indian or Alaska Native 12 (1%) 2 (<1%) 10 (1%) 2 (1%) 0 (0%) 2 (1%)
Asian 55 (3%) 0 (0%) 55 (4%) 4 (1%) 0 (0%) 4 (2%)
Black or African American 24 (1%) 2 (<1%) 22 (2%) 5 (2%) 2 (2%) 3 (2%)
Caucasian 1,403 (80%) 299 (74%) 1,104 (82%) 256 (85%) 82 (77%) 174 (89%)
Hispanic or Latino 111 (6%) 10 (2%) 101 (8%) 12 (4%) 6 (6%) 6 (3%)
Native Hawaiian or Pacific Islander 7 (<1%) 1 (<1%) 6 (<1%) 0 (<1%) 0 (<1%) 0 (<1%)
No information 133 (8%) 89 (22%) 44 (3%) 22 (7%) 16 (15%) 6 (3%)
Open in a new tab
1

Percentages may not total 100 due to rounding.

2

Categories are not mutually exclusive.

Objective 2: Characterize the types of prescription information coded in discrete data fields in the EHR

The EHR data was limited only to prescription data, and did not include pharmacy fill data. We obtained all discrete field medication data. We were able to confirm the presence of information in the directions (sig) of all but 1% of statin prescriptions and 1% of warfarin prescriptions. One percent of statin prescriptions and 4% of warfarin prescriptions were missing information about either medication dose or medication unit. Thirty six percent of statin prescriptions and 51% of warfarin prescriptions were missing information about quantity of medication dispensed. And 36% of each statin or warfarin prescriptions were missing refill information.

Objective 3: Adverse Events Relevant to Medication Prescriptions

We found that 6% of patients prescribed statins and 28% of patients prescribed warfarin medications had evidence of adverse events commonly associated with these medications (see Table 2).

Table 2:

Prevalence detected within the EHR of adverse events among patients prescribed statin medications or warfarin in two primary care organizations

Adults with a Statin Medication Adults with a Warfarin Medication
Overall Organization A Organization B Overall Organization A Organization B
Number of Adults, n 1,745 403 1,342 301 106 195
Statin Complications, Any 104 (6%) 28 (7%) 76 (6%) - - -
Statin myopathy or myopathy diagnosis 57 (3%) 19 (5%) 38 (3%) - - -
Elevated Creatine Kinase2 2 (0%) 0 (0%) 2 (0%) - - -
Elevated ALT3 41 (2%) 9 (2%) 32 (2%) - - -
Elevated AST4 30 (2%) 6 (1%) 24 (2%) - - -
Warfarin Complications, Any - - - 83 (28%) 31 (29%) 52 (27%)
Diagnosis of bleeding complications - - - 51 (17%) 17 (16%) 34 (17%)
Elevated INR5 - - - 45 (15%) 18 (17%) 27 (14%)
Received vitamin K - - - 5 (2%) 5 (5%) 0 (0%)
Open in a new tab
1

The two year post period differed by organization and first medication date.

Among active patients prescribed a statin medication during the first year of the study period, 104 (6%) had evidence of statin myopathy or a myopathy diagnosis coded within one year after the medication prescription date. Among active patients prescribed warfarin, 83 (28%) had evidence of a warfarin-related complication within one year after the medication prescription date.

Discussion

Across two distinct community-based primary care organizations and EHR systems, we were able to identify and describe cohorts of patients prescribed statin medications or warfarin. The population rates of statin use were similar to what has previously been reported using primary care EHR data (Aref-Eshgi 2017, Perlman 2017). We also identified patients with potential adverse events related to these medications. Our approach to identifying cohorts of patients, using string-based search terms of medication names, may be particularly useful for both clinical or quality improvement purposes, particularly in smaller primary care practice settings, where EHR systems may be less sophisticated and NDC information may not be easily accessible. The ability to initially identify patients experiencing medication related adverse events is a critical step for future interventions to reduce these adverse events. These strategies may also be useful for broader clinical research, including cohort identification for clinical trials and epidemiologic research.

We discovered that the EHR-based medication data were frequently missing elements needed to characterize medication treatment course. While EHR-based medication data frequently contained information about the dose of medications, details about the quantity and refills were more often missing. Details for medication instructions were not easily captured and reported and EHRs did not track fill data or patient reports of medication adherence. Without dosing instructions or quantity, the exact amount of medication prescribed cannot be ascertained and the clinical course of treatment is therefore difficult to assess. Improvements in EHR data capture of medication information, such as use of structured data fields for quantity and sig information could address this limitation of EHR data. These limitations may be specific to the EHR systems included in this study.

We were able to identify patients who may have experienced adverse events relevant to either prescription of statin medications or prescription of warfarin. The rate of adverse event identification for both statins and warfarin were comparable to what has previously been reported: in published data, approximately 8–9% of statin users report symptoms consistent with myalgia or myositis, but the rate of severe myositis or rhabdomyolysis is much lower (Nichols 2007). Our rate of adverse statin-relevant event detection (6%) may have been somewhat lower than published reports due to our relatively short observation period. Using ICD-9 and laboratory data, we were able to identify patients with evidence of myositis or rhabdomyolysis, but patients experiencing the symptoms of myalgia may be more readily identified through analysis of free text documentation where nonspecific symptoms are more likely to be reported. We were able to identify 51 patients (17% of those prescribed warfarin) with diagnoses suggestive of any bleeding complication. This is similar to the rate of reported hemorrhage among patients prescribed warfarin (Wittowsky 2004). We were not able to assess the severity of bleeding complication or whether transfusion treatment was required, which is a limitation of the type of data available. The rate of elevated INR among patients prescribed warfarin (15%) is lower than what has been previously reported in the literature, where 20% of patients prescribed warfarin in a primary care setting have had laboratory evidence of elevated INR (Upshur 2003). This difference may be due to the limited observation period (two years) or differences in our patient population or standards of clinical practice in the participating practices. We did not do manual chart review to validate the accuracy of our adverse event detection. This would be an important step to further validate our approach for future research or clinical care applications.

While EHR data may be successfully used to identify cohorts of patients prescribed specific medications or a subset of patients potentially experiencing adverse events related to medication use, additional analysis and exploration is needed to confirm associations between medications and adverse events. Expanded algorithms, which combine natural language processing of text notes with laboratory or diagnostic code fields, may be more successful in identifying cohorts of patients or cases of adverse events with more precision. Exploration of alternate strategies to identify potential outliers, or those at risk for potential adverse medication events, might include searching for patients prescribed a particularly high or low dose of medications or those with early discontinuation of what is typically a chronic medication.

Because EHR data are collected primarily for clinical care and billing, EHR data presents certain limitations when applied to research (Bayley 2013, Coorevits 2013). To address some of these limitations, our team found that an understanding of the clinical context of the data was critical in interpreting our results. For example, the EHR data in Data QUEST is limited to that entered in primary care settings. Patients prescribed a statin or warfarin medication by a specialist may not have been identified using our strategies, unless the medications had been entered in the EHR by the primary care provider. In addition, complications diagnosed by a specialist, emergency room or hospitalization would also not be discovered using primary care data alone.

Our experience using Data QUEST’s EHR data demonstrates the power of EHR data to support clinical research, in particular creating a better understanding of medication adverse events. Based on our experience, EHR data can be used to preliminarily identify a subset of patients potentially experiencing adverse events related to medication use. However, we also highlight important limitations of use of EHR data, such as difficulty assessing medication dose and instructions, which would be needed to accurately describe patients’ treatment courses and identify treatment “outliers” or other important patient subgroups. Our findings should be considered in the future planning and interpretation of clinical research using EHR data related to medications and associated adverse events.

Supplementary Material

Appendix A

Contributor Information

Allison M Cole, Family Medicine, University of Washington School of Medicine.

Kari A. Stepehens, University of Washington, Institute of Translational Health Sciences

Imara West, University of Washington, Institute of Translational Health Sciences.

Gina A. Keppel, University of Washington, Institute of Translational Health Sciences

Ken Thummel, University of Washington.

Laura-Mae Baldwin, University of Washington, Institute of Translational Health Sciences.

References

  1. Aquilante CL, Langaee TY, Lopez LM, et al. Influence of coagulation factor, vitamin K epoxide reductase complex subunit 1, and cytochrome P450 2C9 gene polymorphisms on warfarin dose requirements. Clin Pharmacol Ther 2006;79:291–302. [DOI] [PubMed] [Google Scholar]
  2. Aref-Eshghi E, Oake J, Godwin M, Aubrey-Bassler K, Duke P, Mahdavian M, Asghari S. Identification of Dyslipidemic Patients Attending Primary Care Clinics Using Electronic Medical Record (EMR) Data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) Database. Journal of Medical Systems. 2017. March 1;41(3):45. [DOI] [PubMed] [Google Scholar]
  3. Arwood MJ, Deng J, Drozda K, Pugach O, Nutescu EA, Schmidt S, Duarte JD, Cavallari LH. Anticoagulation endpoints with clinical implementation of warfarin pharmacogentic dosing in a real-world setting: A proposal for a new pharmacogenetic dosing approach. Clin Pharmacol Ther 2017;101(5):675–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bayley KB, Belnap T, Savitz L, Masica AL, Shah N and Fleming NS, 2013. Challenges in using electronic health record data for CER: experience of 4 learning organizations and solutions applied. Medical care, 51, pp.S80–S86. [DOI] [PubMed] [Google Scholar]
  5. Bell GC, Crews KR, Wilkinson MR, Haidar CE, Hicks JK, Baker DK, Kornegay NM, Yang W, Cross SJ, Howard SC, Freimuth RR. Development and use of active clinical decision support for preemptive pharmacogenomics. Journal of the American Medical Informatics Association. 2014. February 1;21(e1):e93–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bibbins-Domingo K, Grossman DC, Curry SJ, Davidson KW, Epling JW, García FA, Gillman MW, Kemper AR, Krist AH, Kurth AE, Landefeld CS. Statin use for the primary prevention of cardiovascular disease in adults: US Preventive Services Task Force recommendation statement. JAMA. 2016. November 15;316(19):1997–2007. [DOI] [PubMed] [Google Scholar]
  7. Coorevits P, Sundgren M, Klein GO, Bahr A, Claerhout B, Daniel C, Dugas M, Dupon D, Schmidt A, Singleton P, DeMoor G, Kalra D. Electronic health records: new opportuneities for clinical research. J Intern Med 2013;274(6):547–60. [DOI] [PubMed] [Google Scholar]
  8. Crystal S, Akincigil A, Bilder S, Walkup JT. Studying prescription drug use and outcomes with Medicaid claims data strengths, limitations, and strategies. Medical care. 2007. October;45(10 SUPL):S58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. DeWilde S, Carey IM, Bremner SA, Richards N, Hilton SR, Cook DG. Evolution of statin prescribing 1994–2001: a case of agism but not of sexism? Heart. 2003. April;89(4):417–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Gaist D, Rodriguez LA, Huerta C, Hallas J, Sindrup SH. Lipid-lowering drugs and risk of myopathy: a population-based follow-up study. Epidemiology. 2001;12(5):565–9. [DOI] [PubMed] [Google Scholar]
  11. Graham DJ, Staffa JA, Shatin D, Andrade SE, Schech SD, La Grenade L, Gurwitz JH, Chan KA, Goodman MJ, Platt R. Incidence of hospitalized rhabdomyolysis in patients treated with lipid-lowering drugs. JAMA. 2004. December 1;292(21):2585–90. [DOI] [PubMed] [Google Scholar]
  12. Ghatak A, Faheem O, Thompson PD. The genetics of statin-induced myopathy. Atherosclerosis. 2010. June;210(2):337–43 [DOI] [PubMed] [Google Scholar]
  13. Go AS, Hylek EM, Chang Y, Phillips KA, Henault LE, Capra AM, Jensvold NG, Selby JV, Singer DE. Anticoagulation therapy for stroke prevention in atrial fibrillation: how well do randomized trials translate into clinical practice? JAMA 2003;290(20):2685–92 [DOI] [PubMed] [Google Scholar]
  14. Gottesman O, Scott SA, Ellis SB, Overby CL, Ludtke A, Hulot JS, Hall J, Chatani K, Myers K, Kannry JL, Bottinger EP. The CLIPMERGE PGx program: clinical implementation of personalized medicine through electronic health records and genomics–pharmacogenomics. Clinical Pharmacology & Therapeutics. 2013. August 1;94(2):214–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics. 2012. June 1;13(6):395–405. [DOI] [PubMed] [Google Scholar]
  16. Nebert DW. Pharmacogenetics and pharmacogenomics: why is this relevant to the clinical geneticist?. Clinical genetics. 1999. October 1;56(4):247–58. [DOI] [PubMed] [Google Scholar]
  17. Nichols GA, Koro CE. Does statin therapy initiation increase the risk for myopathy? An observational study of 32,225 diabetic and nondiabetic patients. Clinical therapeutics. 2007. August 31;29(8):1761–70. [DOI] [PubMed] [Google Scholar]
  18. Oake N, et al. Anticoagulation intensity and outcomes among patients prescribed oral anticoagulant therapy: a systematic review and meta-analysis. CMAJ 2008; 179:235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Office of the National Coordinator for Health Information Technology ‘Office-based Physicians that have Demonstrated Meaningful Use through the Medicare EHR Incentive Program,’ Health IT Dashboard dashboard.healthit.gov/dashboards/physicians-medicare-meaningful-use.php. March 2016
  20. Perlman SE, McVeigh KH, Thorpe LE, Jacobson L, Greene CM, Gwynn RC. Innovations in Population Health Surveillance: Using Electronic Health Records for Chronic Disease Surveillance. American Journal of Public Health. 2017. June(0):e1–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Pladevall M, Williams LK, Potts LA, Divine G, Xi H, Lafata JE. Clinical outcomes and adherence to medications measured by claims data in patients with diabetes. Diabetes care. 2004. December 1;27(12):2800–5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Information Science and Systems. 2014. February 7;2(1):1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Reynolds MW, Fahrbach K, Hauch O, Wygant G, Estok R, Cella C, & Nalysnyk L (2004). Warfarin anticoagulation and outcomes in patients with atrial fibrillation: a systematic review and metaanalysis. CHEST Journal, 126(6), 1938–1945 [DOI] [PubMed] [Google Scholar]
  24. Sathasivam S, Lecky B. Statin induced myopathy. BMJ. 2008;337:a2286. [DOI] [PubMed] [Google Scholar]
  25. Stange KC, Zyzanski SJ, Jaen CR, et al. Illuminating the ‘black box’. A description of 4454 patient visits to 138 family physicians. J Fam Pract. 1998;46:377–389. [PubMed] [Google Scholar]
  26. Stephens KA, Anderson N, Lin C, & Estiri H (in press). Implementing Partnership-driven Clinical Federated Electronic Health Record Data-sharing Networks. International Journal of Medical Informatics. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Stephens KA, Lin CP, Baldwin LM, Echo-Hawk A, Keppel GA, Buchwald D, Whitener RJ, Korngiebel DM, Berg AO, Black RA, Tarczy-Hornoch P. LC Data QUEST: A technical architecture for community federated clinical data sharing. AMIA Jt Summits Transl Sci Proc. 2012;2012:57–62. [PMC free article] [PubMed] [Google Scholar]
  28. Taylor F, Huffman MD, Macedo AF, Moore THM, Burke M, Davey Smith G, Ward K, Ebrahim S. Statins for the primary prevention of cardiovascular disease. Cochrane Database of Systematic Reviews 2013, Issue 1 Art. No.: CD004816. DOI: 10.1002/14651858.CD004816.pub5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Trialists CT. Efficacy of cholesterol-lowering therapy in 18 686 people with diabetes in 14 randomised trials of statins: a meta-analysis. The Lancet. 2008. January 18;371(9607):117–25. [DOI] [PubMed] [Google Scholar]
  30. Upshur RE, Papoushek C, Macdonald D, Dantas G. Short report: managing anticoagulation. Comparison of results at three primary care centres. Canadian family physician. 2003. February;49:181. [PMC free article] [PubMed] [Google Scholar]
  31. Ward S, Lloyd Jones M, Pandor A, Holmes M, Ara R, Ryan A, Yeo W, Payne N. A systematic review and economic evaluation of statins for the prevention of coronary events. Health Techol Assess 2007;11(14):1–160. [DOI] [PubMed] [Google Scholar]
  32. Wittkowsky AK and Devine EB (2004), Frequency and Causes of Overanticoagulation and Underanticoagulation in Patients Treated with Warfarin. Pharmacotherapy, 24: 1311–1316. doi: 10.1592/phco.24.14.1311.43144 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix A
NIHMS1002232-supplement-Appendix_A.docx (50.5KB, docx)

RESOURCES