Design and implementation of electronic health record integrated clinical prediction rules (iCPR): a randomized trial in diverse primary care settings

Clinical prediction rules (CPRs) represent a method of determining individual patient risk to help providers make more accurate decisions at the point of care. Well-validated CPRs are underutilized but may decrease antibiotic overuse for acute respiratory infections. The integrated clinical predicti...

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Published inImplementation science : IS Vol. 12; no. 1; p. 37
Main Authors Feldstein, David A., Hess, Rachel, McGinn, Thomas, Mishuris, Rebecca G., McCullagh, Lauren, Smith, Paul D., Flynn, Michael, Palmisano, Joseph, Doros, Gheorghe, Mann, Devin
Format Journal Article
LanguageEnglish
Published England BioMed Central Ltd 14.03.2017
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Abstract Clinical prediction rules (CPRs) represent a method of determining individual patient risk to help providers make more accurate decisions at the point of care. Well-validated CPRs are underutilized but may decrease antibiotic overuse for acute respiratory infections. The integrated clinical prediction rules (iCPR) study builds on a previous single clinic study to integrate two CPRs into the electronic health record and assess their impact on practice. This article discusses study design and implementation of a multicenter cluster randomized control trial of the iCPR clinical decision support system, including the tool adaptation, usability testing, staff training, and implementation study to disseminate iCPR at multiple clinical sites across two health care systems. The iCPR tool is based on two well-validated CPRs, one for strep pharyngitis and one for pneumonia. The iCPR tool uses the reason for visit to trigger a risk calculator. Provider completion of the risk calculator provides a risk score, which is linked to an order set. Order sets guide evidence-based care and include progress note documentation, tests, prescription medications, and patient instructions. The iCPR tool was refined based on interviews with providers, medical assistants, and clinic managers, and two rounds of usability testing. "Near live" usability testing with simulated patients was used to ensure that iCPR fit into providers' clinical workflows. Thirty-three Family Medicine and General Internal Medicine primary care clinics were recruited at two institutions. Clinics were randomized to academic detailing about strep pharyngitis and pneumonia diagnosis and treatment (control) or academic detailing plus use of the iCPR tool (intervention). The primary outcome is the difference in antibiotic prescribing rates between the intervention and control groups with secondary outcomes of difference in rapid strep and chest x-ray ordering. Use of the components of the iCPR will also be assessed. The iCPR study uses a strong user-centered design and builds on the previous initial study, to assess whether CPRs integrated in the electronic health record can change provider behavior and improve evidence-based care in a broad range of primary care clinics. Clinicaltrials.gov ( NCT02534987 ).
AbstractList Clinical prediction rules (CPRs) represent a method of determining individual patient risk to help providers make more accurate decisions at the point of care. Well-validated CPRs are underutilized but may decrease antibiotic overuse for acute respiratory infections. The integrated clinical prediction rules (iCPR) study builds on a previous single clinic study to integrate two CPRs into the electronic health record and assess their impact on practice. This article discusses study design and implementation of a multicenter cluster randomized control trial of the iCPR clinical decision support system, including the tool adaptation, usability testing, staff training, and implementation study to disseminate iCPR at multiple clinical sites across two health care systems.BACKGROUNDClinical prediction rules (CPRs) represent a method of determining individual patient risk to help providers make more accurate decisions at the point of care. Well-validated CPRs are underutilized but may decrease antibiotic overuse for acute respiratory infections. The integrated clinical prediction rules (iCPR) study builds on a previous single clinic study to integrate two CPRs into the electronic health record and assess their impact on practice. This article discusses study design and implementation of a multicenter cluster randomized control trial of the iCPR clinical decision support system, including the tool adaptation, usability testing, staff training, and implementation study to disseminate iCPR at multiple clinical sites across two health care systems.The iCPR tool is based on two well-validated CPRs, one for strep pharyngitis and one for pneumonia. The iCPR tool uses the reason for visit to trigger a risk calculator. Provider completion of the risk calculator provides a risk score, which is linked to an order set. Order sets guide evidence-based care and include progress note documentation, tests, prescription medications, and patient instructions. The iCPR tool was refined based on interviews with providers, medical assistants, and clinic managers, and two rounds of usability testing. "Near live" usability testing with simulated patients was used to ensure that iCPR fit into providers' clinical workflows. Thirty-three Family Medicine and General Internal Medicine primary care clinics were recruited at two institutions. Clinics were randomized to academic detailing about strep pharyngitis and pneumonia diagnosis and treatment (control) or academic detailing plus use of the iCPR tool (intervention). The primary outcome is the difference in antibiotic prescribing rates between the intervention and control groups with secondary outcomes of difference in rapid strep and chest x-ray ordering. Use of the components of the iCPR will also be assessed.METHODSThe iCPR tool is based on two well-validated CPRs, one for strep pharyngitis and one for pneumonia. The iCPR tool uses the reason for visit to trigger a risk calculator. Provider completion of the risk calculator provides a risk score, which is linked to an order set. Order sets guide evidence-based care and include progress note documentation, tests, prescription medications, and patient instructions. The iCPR tool was refined based on interviews with providers, medical assistants, and clinic managers, and two rounds of usability testing. "Near live" usability testing with simulated patients was used to ensure that iCPR fit into providers' clinical workflows. Thirty-three Family Medicine and General Internal Medicine primary care clinics were recruited at two institutions. Clinics were randomized to academic detailing about strep pharyngitis and pneumonia diagnosis and treatment (control) or academic detailing plus use of the iCPR tool (intervention). The primary outcome is the difference in antibiotic prescribing rates between the intervention and control groups with secondary outcomes of difference in rapid strep and chest x-ray ordering. Use of the components of the iCPR will also be assessed.The iCPR study uses a strong user-centered design and builds on the previous initial study, to assess whether CPRs integrated in the electronic health record can change provider behavior and improve evidence-based care in a broad range of primary care clinics.DISCUSSIONThe iCPR study uses a strong user-centered design and builds on the previous initial study, to assess whether CPRs integrated in the electronic health record can change provider behavior and improve evidence-based care in a broad range of primary care clinics.Clinicaltrials.gov ( NCT02534987 ).TRIAL REGISTRATIONClinicaltrials.gov ( NCT02534987 ).
Clinical prediction rules (CPRs) represent a method of determining individual patient risk to help providers make more accurate decisions at the point of care. Well-validated CPRs are underutilized but may decrease antibiotic overuse for acute respiratory infections. The integrated clinical prediction rules (iCPR) study builds on a previous single clinic study to integrate two CPRs into the electronic health record and assess their impact on practice. This article discusses study design and implementation of a multicenter cluster randomized control trial of the iCPR clinical decision support system, including the tool adaptation, usability testing, staff training, and implementation study to disseminate iCPR at multiple clinical sites across two health care systems. The iCPR tool is based on two well-validated CPRs, one for strep pharyngitis and one for pneumonia. The iCPR tool uses the reason for visit to trigger a risk calculator. Provider completion of the risk calculator provides a risk score, which is linked to an order set. Order sets guide evidence-based care and include progress note documentation, tests, prescription medications, and patient instructions. The iCPR tool was refined based on interviews with providers, medical assistants, and clinic managers, and two rounds of usability testing. "Near live" usability testing with simulated patients was used to ensure that iCPR fit into providers' clinical workflows. Thirty-three Family Medicine and General Internal Medicine primary care clinics were recruited at two institutions. Clinics were randomized to academic detailing about strep pharyngitis and pneumonia diagnosis and treatment (control) or academic detailing plus use of the iCPR tool (intervention). The primary outcome is the difference in antibiotic prescribing rates between the intervention and control groups with secondary outcomes of difference in rapid strep and chest x-ray ordering. Use of the components of the iCPR will also be assessed. The iCPR study uses a strong user-centered design and builds on the previous initial study, to assess whether CPRs integrated in the electronic health record can change provider behavior and improve evidence-based care in a broad range of primary care clinics. Clinicaltrials.gov ( NCT02534987 ).
Clinical prediction rules (CPRs) represent a method of determining individual patient risk to help providers make more accurate decisions at the point of care. Well-validated CPRs are underutilized but may decrease antibiotic overuse for acute respiratory infections. The integrated clinical prediction rules (iCPR) study builds on a previous single clinic study to integrate two CPRs into the electronic health record and assess their impact on practice. This article discusses study design and implementation of a multicenter cluster randomized control trial of the iCPR clinical decision support system, including the tool adaptation, usability testing, staff training, and implementation study to disseminate iCPR at multiple clinical sites across two health care systems. The iCPR tool is based on two well-validated CPRs, one for strep pharyngitis and one for pneumonia. The iCPR tool uses the reason for visit to trigger a risk calculator. Provider completion of the risk calculator provides a risk score, which is linked to an order set. Order sets guide evidence-based care and include progress note documentation, tests, prescription medications, and patient instructions. The iCPR tool was refined based on interviews with providers, medical assistants, and clinic managers, and two rounds of usability testing. "Near live" usability testing with simulated patients was used to ensure that iCPR fit into providers' clinical workflows. Thirty-three Family Medicine and General Internal Medicine primary care clinics were recruited at two institutions. Clinics were randomized to academic detailing about strep pharyngitis and pneumonia diagnosis and treatment (control) or academic detailing plus use of the iCPR tool (intervention). The primary outcome is the difference in antibiotic prescribing rates between the intervention and control groups with secondary outcomes of difference in rapid strep and chest x-ray ordering. Use of the components of the iCPR will also be assessed. The iCPR study uses a strong user-centered design and builds on the previous initial study, to assess whether CPRs integrated in the electronic health record can change provider behavior and improve evidence-based care in a broad range of primary care clinics.
Background Clinical prediction rules (CPRs) represent a method of determining individual patient risk to help providers make more accurate decisions at the point of care. Well-validated CPRs are underutilized but may decrease antibiotic overuse for acute respiratory infections. The integrated clinical prediction rules (iCPR) study builds on a previous single clinic study to integrate two CPRs into the electronic health record and assess their impact on practice. This article discusses study design and implementation of a multicenter cluster randomized control trial of the iCPR clinical decision support system, including the tool adaptation, usability testing, staff training, and implementation study to disseminate iCPR at multiple clinical sites across two health care systems. Methods The iCPR tool is based on two well-validated CPRs, one for strep pharyngitis and one for pneumonia. The iCPR tool uses the reason for visit to trigger a risk calculator. Provider completion of the risk calculator provides a risk score, which is linked to an order set. Order sets guide evidence-based care and include progress note documentation, tests, prescription medications, and patient instructions. The iCPR tool was refined based on interviews with providers, medical assistants, and clinic managers, and two rounds of usability testing. "Near live" usability testing with simulated patients was used to ensure that iCPR fit into providers' clinical workflows. Thirty-three Family Medicine and General Internal Medicine primary care clinics were recruited at two institutions. Clinics were randomized to academic detailing about strep pharyngitis and pneumonia diagnosis and treatment (control) or academic detailing plus use of the iCPR tool (intervention). The primary outcome is the difference in antibiotic prescribing rates between the intervention and control groups with secondary outcomes of difference in rapid strep and chest x-ray ordering. Use of the components of the iCPR will also be assessed. Discussion The iCPR study uses a strong user-centered design and builds on the previous initial study, to assess whether CPRs integrated in the electronic health record can change provider behavior and improve evidence-based care in a broad range of primary care clinics. Trial registration Clinicaltrials.gov (NCT02534987) Keywords: Clinical decision support, Electronic health record, Implementation science, Pneumonia, Pharyngitis, Randomized controlled trial, Streptococcal infections
Background Clinical prediction rules (CPRs) represent a method of determining individual patient risk to help providers make more accurate decisions at the point of care. Well-validated CPRs are underutilized but may decrease antibiotic overuse for acute respiratory infections. The integrated clinical prediction rules (iCPR) study builds on a previous single clinic study to integrate two CPRs into the electronic health record and assess their impact on practice. This article discusses study design and implementation of a multicenter cluster randomized control trial of the iCPR clinical decision support system, including the tool adaptation, usability testing, staff training, and implementation study to disseminate iCPR at multiple clinical sites across two health care systems. Methods The iCPR tool is based on two well-validated CPRs, one for strep pharyngitis and one for pneumonia. The iCPR tool uses the reason for visit to trigger a risk calculator. Provider completion of the risk calculator provides a risk score, which is linked to an order set. Order sets guide evidence-based care and include progress note documentation, tests, prescription medications, and patient instructions. The iCPR tool was refined based on interviews with providers, medical assistants, and clinic managers, and two rounds of usability testing. “Near live” usability testing with simulated patients was used to ensure that iCPR fit into providers’ clinical workflows. Thirty-three Family Medicine and General Internal Medicine primary care clinics were recruited at two institutions. Clinics were randomized to academic detailing about strep pharyngitis and pneumonia diagnosis and treatment (control) or academic detailing plus use of the iCPR tool (intervention). The primary outcome is the difference in antibiotic prescribing rates between the intervention and control groups with secondary outcomes of difference in rapid strep and chest x-ray ordering. Use of the components of the iCPR will also be assessed. Discussion The iCPR study uses a strong user-centered design and builds on the previous initial study, to assess whether CPRs integrated in the electronic health record can change provider behavior and improve evidence-based care in a broad range of primary care clinics.
ArticleNumber 37
Audience Academic
Author Mishuris, Rebecca G.
Smith, Paul D.
Palmisano, Joseph
Mann, Devin
McCullagh, Lauren
Hess, Rachel
Feldstein, David A.
McGinn, Thomas
Doros, Gheorghe
Flynn, Michael
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Cites_doi 10.1001/jama.2009.1163
10.7326/0003-4819-113-9-664
10.1086/511159
10.1177/1524839910385897
10.1136/bmjopen-2015-009957
10.1001/archinternmed.2011.772
10.1136/bmj.f657
10.1001/jama.2013.286141
10.1093/her/cyl081
10.1001/jama.284.1.79
10.1197/jamia.M3085
10.1001/jama.2016.4151
10.1001/jama.291.13.1587
10.1001/jama.284.22.2912
10.1056/NEJMsa022615
10.1001/archinternmed.2008.551
10.1016/j.pec.2011.01.002
10.1001/jamainternmed.2013.8980
10.1177/0272989X8100100304
10.1186/1471-2296-8-42
10.1001/jamainternmed.2013.11673
10.1123/jsep.33.2.198
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Issue 1
Keywords Pneumonia
Implementation science
Streptococcal infections
Clinical decision support
Electronic health record
Pharyngitis
Randomized controlled trial
Language English
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References J Ammentorp (567_CR22) 2011; 82
EA McGlynn (567_CR1) 2003; 348
MH Ebell (567_CR19) 2007; 76
CG Grijalva (567_CR5) 2009; 302
TG McGinn (567_CR9) 2013; 173
WJ McIsaac (567_CR11) 2004; 291
PS Heckerling (567_CR12) 1990; 113
TG McGinn (567_CR3) 2000; 284
KE Fleming-Dutra (567_CR8) 2016; 315
RE Glasgow (567_CR20) 2006; 21
D Korenstein (567_CR2) 2012; 172
ML Barnett (567_CR6) 2014; 311
I Antikainen (567_CR24) 2011; 33
E Wallace (567_CR4) 2016; 6
LA Mandell (567_CR14) 2007; 44
S Bakken (567_CR23) 2009; 16
ML Barnett (567_CR7) 2014; 174
PS Roshanov (567_CR16) 2013; 346
RM Centor (567_CR10) 1981; 1
CR May (567_CR25) 2007; 8
BN Doebbeling (567_CR15) 2011
T Isaac (567_CR17) 2009; 169
WJ McIsaac (567_CR13) 1998; 158
MH Ebell (567_CR18) 2000; 284
B Estabrook (567_CR21) 2012; 13
10872017 - JAMA. 2000 Jul 5;284(1):79-84
21441205 - Health Promot Pract. 2012 Mar;13(2):190-7
21306855 - Patient Educ Couns. 2011 Mar;82(3):482-7
19717799 - J Am Med Inform Assoc. 2009 Nov-Dec;16(6):889-97
12826639 - N Engl J Med. 2003 Jun 26;348(26):2635-45
15069046 - JAMA. 2004 Apr 7;291(13):1587-95
16945984 - Health Educ Res. 2006 Oct;21(5):688-94
24846041 - JAMA. 2014 May 21;311(19):2020-2
17278083 - Clin Infect Dis. 2007 Mar 1;44 Suppl 2:S27-72
17650326 - BMC Fam Pract. 2007 Jul 24;8:42
23412440 - BMJ. 2013 Feb 14;346:f657
24091806 - JAMA Intern Med. 2014 Jan;174(1):138-40
27008685 - BMJ Open. 2016 Mar 15;6(3):e009957
19690308 - JAMA. 2009 Aug 19;302(7):758-66
6763125 - Med Decis Making. 1981;1(3):239-46
17853631 - Am Fam Physician. 2007 Aug 15;76(4):560-2
21558580 - J Sport Exerc Psychol. 2011 Apr;33(2):198-214
19204222 - Arch Intern Med. 2009 Feb 9;169(3):305-11
11147989 - JAMA. 2000 Dec 13;284(22):2912-8
2221647 - Ann Intern Med. 1990 Nov 1;113(9):664-70
27139059 - JAMA. 2016 May 3;315(17):1864-73
9475915 - CMAJ. 1998 Jan 13;158(1):75-83
23896675 - JAMA Intern Med. 2013 Sep 23;173(17):1584-91
22271125 - Arch Intern Med. 2012 Jan 23;172(2):171-8
References_xml – volume: 302
  start-page: 758
  year: 2009
  ident: 567_CR5
  publication-title: JAMA
  doi: 10.1001/jama.2009.1163
– volume: 113
  start-page: 664
  year: 1990
  ident: 567_CR12
  publication-title: Ann Intern Med
  doi: 10.7326/0003-4819-113-9-664
– volume: 44
  start-page: S27
  year: 2007
  ident: 567_CR14
  publication-title: Clin Infect Dis
  doi: 10.1086/511159
– volume: 13
  start-page: 190
  year: 2012
  ident: 567_CR21
  publication-title: Health Promot Pract
  doi: 10.1177/1524839910385897
– volume: 6
  start-page: e009957
  year: 2016
  ident: 567_CR4
  publication-title: BMJ Open
  doi: 10.1136/bmjopen-2015-009957
– volume: 172
  start-page: 171
  year: 2012
  ident: 567_CR2
  publication-title: Arch Intern Med
  doi: 10.1001/archinternmed.2011.772
– volume: 346
  start-page: f657
  year: 2013
  ident: 567_CR16
  publication-title: BMJ
  doi: 10.1136/bmj.f657
– volume: 311
  start-page: 2020
  year: 2014
  ident: 567_CR6
  publication-title: JAMA
  doi: 10.1001/jama.2013.286141
– volume: 21
  start-page: 688
  year: 2006
  ident: 567_CR20
  publication-title: Health Educ Res
  doi: 10.1093/her/cyl081
– volume: 284
  start-page: 79
  year: 2000
  ident: 567_CR3
  publication-title: JAMA
  doi: 10.1001/jama.284.1.79
– volume: 16
  start-page: 889
  year: 2009
  ident: 567_CR23
  publication-title: J Am Med Inform Assoc
  doi: 10.1197/jamia.M3085
– volume: 315
  start-page: 1864
  year: 2016
  ident: 567_CR8
  publication-title: JAMA
  doi: 10.1001/jama.2016.4151
– volume: 291
  start-page: 1587
  year: 2004
  ident: 567_CR11
  publication-title: JAMA
  doi: 10.1001/jama.291.13.1587
– volume: 284
  start-page: 2912
  year: 2000
  ident: 567_CR18
  publication-title: JAMA
  doi: 10.1001/jama.284.22.2912
– volume: 348
  start-page: 2635
  year: 2003
  ident: 567_CR1
  publication-title: N Engl J Med
  doi: 10.1056/NEJMsa022615
– volume: 169
  start-page: 305
  year: 2009
  ident: 567_CR17
  publication-title: Arch Intern Med
  doi: 10.1001/archinternmed.2008.551
– volume: 82
  start-page: 482
  year: 2011
  ident: 567_CR22
  publication-title: Patient Educ Couns
  doi: 10.1016/j.pec.2011.01.002
– volume: 173
  start-page: 1584
  year: 2013
  ident: 567_CR9
  publication-title: JAMA Intern Med
  doi: 10.1001/jamainternmed.2013.8980
– volume: 1
  start-page: 239
  year: 1981
  ident: 567_CR10
  publication-title: Med Decis Making
  doi: 10.1177/0272989X8100100304
– volume: 8
  start-page: 42
  year: 2007
  ident: 567_CR25
  publication-title: BMC Fam Pract
  doi: 10.1186/1471-2296-8-42
– volume: 174
  start-page: 138
  year: 2014
  ident: 567_CR7
  publication-title: JAMA Intern Med
  doi: 10.1001/jamainternmed.2013.11673
– volume: 158
  start-page: 75
  year: 1998
  ident: 567_CR13
  publication-title: CMAJ
– volume-title: Integrating clinical decision support into workflow—final report. AHRQ Publication no. 11-0076-EF
  year: 2011
  ident: 567_CR15
– volume: 76
  start-page: 560
  year: 2007
  ident: 567_CR19
  publication-title: Am Fam Physician
– volume: 33
  start-page: 198
  year: 2011
  ident: 567_CR24
  publication-title: J Sport Exerc Psychol
  doi: 10.1123/jsep.33.2.198
– reference: 21558580 - J Sport Exerc Psychol. 2011 Apr;33(2):198-214
– reference: 9475915 - CMAJ. 1998 Jan 13;158(1):75-83
– reference: 23896675 - JAMA Intern Med. 2013 Sep 23;173(17):1584-91
– reference: 23412440 - BMJ. 2013 Feb 14;346:f657
– reference: 19690308 - JAMA. 2009 Aug 19;302(7):758-66
– reference: 2221647 - Ann Intern Med. 1990 Nov 1;113(9):664-70
– reference: 24091806 - JAMA Intern Med. 2014 Jan;174(1):138-40
– reference: 15069046 - JAMA. 2004 Apr 7;291(13):1587-95
– reference: 21306855 - Patient Educ Couns. 2011 Mar;82(3):482-7
– reference: 27139059 - JAMA. 2016 May 3;315(17):1864-73
– reference: 16945984 - Health Educ Res. 2006 Oct;21(5):688-94
– reference: 24846041 - JAMA. 2014 May 21;311(19):2020-2
– reference: 19204222 - Arch Intern Med. 2009 Feb 9;169(3):305-11
– reference: 17650326 - BMC Fam Pract. 2007 Jul 24;8:42
– reference: 21441205 - Health Promot Pract. 2012 Mar;13(2):190-7
– reference: 10872017 - JAMA. 2000 Jul 5;284(1):79-84
– reference: 12826639 - N Engl J Med. 2003 Jun 26;348(26):2635-45
– reference: 22271125 - Arch Intern Med. 2012 Jan 23;172(2):171-8
– reference: 11147989 - JAMA. 2000 Dec 13;284(22):2912-8
– reference: 17278083 - Clin Infect Dis. 2007 Mar 1;44 Suppl 2:S27-72
– reference: 19717799 - J Am Med Inform Assoc. 2009 Nov-Dec;16(6):889-97
– reference: 17853631 - Am Fam Physician. 2007 Aug 15;76(4):560-2
– reference: 27008685 - BMJ Open. 2016 Mar 15;6(3):e009957
– reference: 6763125 - Med Decis Making. 1981;1(3):239-46
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Snippet Clinical prediction rules (CPRs) represent a method of determining individual patient risk to help providers make more accurate decisions at the point of care....
Background Clinical prediction rules (CPRs) represent a method of determining individual patient risk to help providers make more accurate decisions at the...
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StartPage 37
SubjectTerms Adolescent
Adult
Age
Aged
Ambulatory care
Anti-Bacterial Agents - therapeutic use
Antibiotics
Child
Child, Preschool
Clinics
Cluster Analysis
Decision making
Decision Support Techniques
Electronic health records
Electronic Health Records - statistics & numerical data
Human subjects
Humans
Information management
Internal medicine
Intervention
Management
Medical records
Medical research
Medicine
Middle Aged
Online instruction
Patients
Physicians
Pneumonia
Practice Patterns, Physicians' - statistics & numerical data
Primary care
Primary health care
Primary Health Care - methods
Primary Health Care - statistics & numerical data
Respiratory Tract Infections - drug therapy
Review boards
Streptococcus infections
Studies
Study Protocol
Technology application
Young Adult
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Title Design and implementation of electronic health record integrated clinical prediction rules (iCPR): a randomized trial in diverse primary care settings
URI https://www.ncbi.nlm.nih.gov/pubmed/28292304
https://www.proquest.com/docview/1883914743
https://www.proquest.com/docview/1877851624
https://pubmed.ncbi.nlm.nih.gov/PMC5351194
Volume 12
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