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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT04147832
Other study ID # Pro00037143
Secondary ID
Status Completed
Phase
First received
Last updated
Start date October 5, 2020
Est. completion date July 31, 2021

Study information

Verified date August 2021
Source Epividian
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

The primary objective of this study is to evaluate the effectiveness of a clinical decision support system (CDSS) combined with enhanced patient contact to retain HIV+ patients in care with AIDS Healthcare Foundation. Specifically, the study aims to evaluate the effectiveness of having the patient's primary caregiver (or dedicated case manager) telephone the patient when the patient is identified as a significant risk to loss of follow-up (at-risk patients) based upon pre-defined criteria. The secondary objective Gain a better understanding about the implementation of the study's procedures in clinical practice by evaluating survey responses delivered to participating healthcare providers and AHF staff members engaging with the study's intervention.


Description:

Retention in care and re-engagement in care is a primary concern in HIV treatment today and a major obstacle in the US to reach the UNAIDS 90-90-90 goal [1]. The U.S. CDC supports the use of HIV surveillance data to identify not-in-care (NIC) patients and re-link them to care (Data-to-Care). The optimal strategy for identifying patients for Data-to-Care is unknown. It has been postulated that by increasing follow up of high-risk patients not meeting the retention in care measures, the number of people living with HIV being retained in care may be increased by 10%. Multiple HIV clinics within the AHF organization in the US are eligible for study participation. "Primary" HIV provider will be defined as the primary physician or advanced care practitioner following a patient, as recorded in their respective EHRs and identified through CHORUS, a CDSS developed by Epividian. The CDSS will track patient case status as active or inactive (loss to follow-up, transferred medical care, or deceased). Providers will be informed of the study and sites will be contracted to participate in this collaborative research study. The CDSS will generate alerts to the providers warning of suboptimal patient attendance among the population. The alerts will be generated using the following four criteria of at-risk of loss to follow-up: - At Risk #1: No visit in the previous 4 months and no scheduled appointment in the subsequent 2 months. - High Risk #2: Single appointment in the previous year, a missed appointment in the previous month and no scheduled appointment in the next 2 months. - High Risk #3: Those with 2 missed sequential appointments, and no scheduled appointment in the subsequent 7 days. - High Risk #4: Those with an attended appointment >3 months ago and their most recent viral load >1000 copies/ml.


Recruitment information / eligibility

Status Completed
Enrollment 10521
Est. completion date July 31, 2021
Est. primary completion date July 31, 2021
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - HIV-1+ - 18 years old or older - Any sex - Seen at least once in a US-based AHF clinic in the last 2 years with care documented in the EHR Exclusion Criteria: - Patients known to have left the practice - Patients who choose not to be contacted about their care via telephone

Study Design


Related Conditions & MeSH terms


Intervention

Other:
Alert to provider
Provider will receive an alert of sub-optimal patient attendance using 4 rules.

Locations

Country Name City State
United States AHF Los Angeles California

Sponsors (3)

Lead Sponsor Collaborator
Epividian AIDS Healthcare Foundation, ViiV Healthcare

Country where clinical trial is conducted

United States, 

References & Publications (26)

Bakken S, Cimino JJ, Hripcsak G. Promoting patient safety and enabling evidence-based practice through informatics. Med Care. 2004 Feb;42(2 Suppl):II49-56. — View Citation

Bakken S, Roberts WD, Chen E, Dilone J, Lee NJ, Mendonca E, Markatou M. PDA-based informatics strategies for tobacco use screening and smoking cessation management: a case study. Stud Health Technol Inform. 2007;129(Pt 2):1447-51. — View Citation

Bakken S. An informatics infrastructure is essential for evidence-based practice. J Am Med Inform Assoc. 2001 May-Jun;8(3):199-201. — View Citation

Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med. 2003 Jun 19;348(25):2526-34. — View Citation

Bindoff IK, Tenni PC, Peterson GM, Kang BH, Jackson SL. Development of an intelligent decision support system for medication review. J Clin Pharm Ther. 2007 Feb;32(1):81-8. — View Citation

Cowen M, Halasyamani LK, McMurtrie D, Hoffman D, Polley T, Alexander JA. Organizational structure for addressing the attributes of the ideal healthcare delivery system. J Healthc Manag. 2008 Nov-Dec;53(6):407-18; discussion 419. — View Citation

Crawford TN. Poor retention in care one-year after viral suppression: a significant predictor of viral rebound. AIDS Care. 2014;26(11):1393-9. doi: 10.1080/09540121.2014.920076. Epub 2014 May 21. — View Citation

Dickey J, Girard DE, Geheb MA, Cassel CK. Using systems-based practice to integrate education and clinical services. Med Teach. 2004 Aug;26(5):428-34. — View Citation

Doebbeling BN, Chou AF, Tierney WM. Priorities and strategies for the implementation of integrated informatics and communications technology to improve evidence-based practice. J Gen Intern Med. 2006 Feb;21 Suppl 2:S50-7. — View Citation

Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, Sam J, Haynes RB. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005 Mar 9;293(10):1223-38. Review. — View Citation

Grant RW, Wald JS, Poon EG, Schnipper JL, Gandhi TK, Volk LA, Middleton B. Design and implementation of a web-based patient portal linked to an ambulatory care electronic health record: patient gateway for diabetes collaborative care. Diabetes Technol Ther. 2006 Oct;8(5):576-86. — View Citation

Hemming K, Haines TP, Chilton PJ, Girling AJ, Lilford RJ. The stepped wedge cluster randomised trial: rationale, design, analysis, and reporting. BMJ. 2015 Feb 6;350:h391. doi: 10.1136/bmj.h391. Review. — View Citation

Hsieh TC, Kuperman GJ, Jaggi T, Hojnowski-Diaz P, Fiskio J, Williams DH, Bates DW, Gandhi TK. Characteristics and consequences of drug allergy alert overrides in a computerized physician order entry system. J Am Med Inform Assoc. 2004 Nov-Dec;11(6):482-91. Epub 2004 Aug 6. — View Citation

Jenders RA, Osheroff JA, Sittig DF, Pifer EA, Teich JM. Recommendations for clinical decision support deployment: synthesis of a roundtable of medical directors of information systems. AMIA Annu Symp Proc. 2007 Oct 11:359-63. — View Citation

Kazanjian P. UNAIDS 90-90-90 Campaign to End the AIDS Epidemic in Historic Perspective. Milbank Q. 2017 Jun;95(2):408-439. doi: 10.1111/1468-0009.12265. — View Citation

Lester WT, Ashburner JM, Grant RW, Chueh HC, Barry MJ, Atlas SJ. Mammography FastTrack: an intervention to facilitate reminders for breast cancer screening across a heterogeneous multi-clinic primary care network. J Am Med Inform Assoc. 2009 Mar-Apr;16(2):187-95. doi: 10.1197/jamia.M2813. Epub 2008 Dec 11. — View Citation

Lester WT, Grant R, Barnett GO, Chueh H. Facilitated lipid management using interactive e-mail: preliminary results of a randomized controlled trial. Stud Health Technol Inform. 2004;107(Pt 1):232-6. — View Citation

Lester WT, Grant RW, Barnett GO, Chueh HC. Randomized controlled trial of an informatics-based intervention to increase statin prescription for secondary prevention of coronary disease. J Gen Intern Med. 2006 Jan;21(1):22-9. — View Citation

McGowan JJ, Cusack CM, Poon EG. Formative evaluation: a critical component in EHR implementation. J Am Med Inform Assoc. 2008 May-Jun;15(3):297-301. doi: 10.1197/jamia.M2584. Epub 2008 Feb 28. — View Citation

Middleton B, Hammond WE, Brennan PF, Cooper GF. Accelerating U.S. EHR adoption: how to get there from here. recommendations based on the 2004 ACMI retreat. J Am Med Inform Assoc. 2005 Jan-Feb;12(1):13-9. Epub 2004 Oct 18. — View Citation

Poon EG, Wald J, Bates DW, Middleton B, Kuperman GJ, Gandhi TK. Supporting patient care beyond the clinical encounter: three informatics innovations from partners health care. AMIA Annu Symp Proc. 2003:1072. — View Citation

Rind DM, Safran C, Phillips RS, Wang Q, Calkins DR, Delbanco TL, Bleich HL, Slack WV. Effect of computer-based alerts on the treatment and outcomes of hospitalized patients. Arch Intern Med. 1994 Jul 11;154(13):1511-7. — View Citation

Robertson M, Laraque F, Mavronicolas H, Braunstein S, Torian L. Linkage and retention in care and the time to HIV viral suppression and viral rebound - New York City. AIDS Care. 2015;27(2):260-7. doi: 10.1080/09540121.2014.959463. Epub 2014 Sep 22. — View Citation

Ruland CM, Bakken S. Developing, implementing, and evaluating decision support systems for shared decision making in patient care: a conceptual model and case illustration. J Biomed Inform. 2002 Oct-Dec;35(5-6):313-21. — View Citation

Schulman J, Kuperman GJ, Kharbanda A, Kaushal R. Discovering how to think about a hospital patient information system by struggling to evaluate it: a committee's journal. J Am Med Inform Assoc. 2007 Sep-Oct;14(5):537-41. Epub 2007 Jun 28. — View Citation

Yehia BR, French B, Fleishman JA, Metlay JP, Berry SA, Korthuis PT, Agwu AL, Gebo KA; HIV Research Network. Retention in care is more strongly associated with viral suppression in HIV-infected patients with lower versus higher CD4 counts. J Acquir Immune Defic Syndr. 2014 Mar 1;65(3):333-9. doi: 10.1097/QAI.0000000000000023. — View Citation

* Note: There are 26 references in allClick here to view all references

Outcome

Type Measure Description Time frame Safety issue
Primary Rate of Kept Appointments Pre and post-baseline rate of patients who attended their scheduled office visits. 16 months, 01-Nov-2019 to 30-Apr-2021
Secondary Viral suppression Proportion of patients with an undetectable viral load defined as patients with a viral load <50 copies/mL out of all patients seen at the practice in the past 2 years 16 months, 01-Nov-2019 to 30-Apr-2021
Secondary Ranked Scoring of Implementation effectiveness Evaluation of healthcare providers' survey responses regarding implementation context of appropriateness, feasibility, adoption, appropriateness and effectiveness. With providers' scores ranked 1 (lowest/worst) to 5 (highest/best), will report pre and post-baseline averages and distributions of scores. 16 months, 01-Nov-2019 to 30-Apr-2021
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