Hospital Acquired ConditionNCT03911687
(CONCERN) Clinical Decision Support (CDS) System
A study testing CONCERN CDS system notification in people with Hospital Acquired Condition, run by Columbia University at 4 sites in United States.
Trial summary
There are patients who die or have a bad outcome in the hospital and this could be prevented. Data in the nurses' notes could be used by computers to tell the rest of the care team that a patient is not doing well and that they should act more quickly. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Hospital Acquired Condition
- What is being tested?
- CONCERN CDS system notification
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 60,893 participants
- Where
- 4 sitesUnited States
- Study dates
- 21 Jul 2020to 31 Oct 2022
- Phase
- Interventional
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study inclusion criteria for patients:
- Inpatients with a stay of at least 24 hours on one of our study units
Study exclusion criteria for patients:
- Inpatients with less than 24 hours on one of our study units
- Patients less than 18 years of age
- Hospice patients
- Did not have a hospital encounter, patients not on one of our study units.
Definition of Study Units:
A clinical unit is considered a CONCERN Study unit if it meets the following criteria:
- A general medical or surgical acute care or critical care unit
The following clinical units are NOT considered CONCERN Study units:
- Pediatric or Neonatal units
- Hospice units
- Emergency Department
- Oncology units
- Obstetrician (OB)/labor and delivery units
- Behavioral/psych units
- Observational units
- Operating room
- Pre-op
- Post-op/Post Anesthesia Care Unit (PACU)
- Same day surgical units
- Plastics units
- Virtual departments in EHR database.
Trial locations and contacts
4 sites in 1 country.
United States4 sites
Brigham and Women's Hospital
Boston, Massachusetts, 02115
New York Presbyterian Columbia University Medical Center
New York, New York, 10032
New York Presbyterian Allen Hospital
New York, New York, 10034
Newton-Wellesley Hospital
Newton, Massachusetts, 02462
About this study
Official title: Communicating Narrative Concerns Entered by RNs (CONCERN) Clinical Decision Support (CDS) System
There are patients who die or have a bad outcome in the hospital and this could be prevented. Data in the nurses' notes could be used by computers to tell the rest of the care team that a patient is not doing well and that they should act more quickly. This project will build and evaluate a computer system that makes it easier for the care team to see and understand that data and act quickly to save patients. The aims of this study is to answer the questions, what is the level of provider use of the CONCERN CDS notification system (called CONCERN SMARTapp) and resulting impact on selected patient outcomes? Specifically, the study has 1) validated desired thresholds for the CONCERN CDS system and 2) integrated the CONCERN CDS system for early warning of risky patient states within CDS tools.
In this portion of the study (aim 3), the investigator will implement and evaluate the CONCERN CDS system on primary outcomes of in-hospital mortality and length of stay and secondary outcomes of cardiac arrest, unanticipated transfers to the intensive care unit, and 30-day hospital readmission rates.
Read the detailed description
Annually, more than 200,000 patients die in U.S. hospitals from cardiac arrest and over 130,000 patients inpatients deaths are attributed to sepsis. These deaths are preventable if patients who are at risk are detected earlier. Prior work found that nursing documentation within electronic health records (EHRs) contains information that could contribute to early detection and treatment, but these data are not being analyzed and exposed by EHRs to clinicians to initiate interventions quickly enough to save patients. A new source of predictive data is defined by analyzing the frequency and types of nursing documentation that indicated nurses' increased surveillance and level of concern for a patient. These data documented in the 48 hours preceding a cardiac arrest and hospital mortality were predictive of the event. While clinicians strive to provide the best care, there is a systematic problem within hospital settings of non-optimal communication between nurses and doctors leading to delays in care for patient at risk. Well-designed and tested EHRs are able to trend data and support communication and decision making, but too often fall short of these goals and actually increase clinician cognitive load through fragmented information displays, "note bloat", and information overload. Substitutable Medical Applications \& Reusable Technologies (SMARTapps) using Fast Health Interoperability Resource (FHIR) standard allow for open sharing and use of innovations across EHR systems. The aim of this project is to design and evaluate a SMARTapp on FHIR used across two large academic medical centers that exposes to physicians and nurses our new predictive data source from nursing documentation to increase care team situational awareness of at risk patients to decrease preventable adverse outcomes.
Communicating Narrative Concerns Entered by RNs (CONCERN) Clinical Decision Support (CDS) system is the application being designed and evaluated. CONCERN Intervention Trial Design will be a multiple time-series intervention. Baseline data will be collected at all study sites. Silent release mode (no SMARTapp notification) will be used in non-equivalent control units and as a post-intervention unit control to evaluate if notifying clinicians can decrease rates of length of stay on non-ICU units and rates of 30-day hospital readmissions. Different versions of the CDS system (SMARTapp) will be incorporated for dynamic, adaptive functionality and determine if the pattern of nursing documentation has changed. A "burn-in" phase is built in to evaluate adoption and adaptation to the algorithm and phases for deployment of the silent release mode within the multiple time-series intervention trial for a total of 18 months of data collection, including pre-intervention data collection and silent release modes.
- Conditions
- Hospital Acquired Condition
- Being tested
- CONCERN CDS system notification
- Keywords
- Early warning system, Clinical decision support, Predictive analysis, Failure to rescue, Inter-professional situational awareness, Nursing surveillance, Healthcare process models of clinical concern (HPM-CC)
- Medical subject headings
- Iatrogenic Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CONCERN CDS system notificationBehavioural
Also called CONCERN SMARTapp notification, CONCERN Clinical Decision Support (CDS) system notification
The CONCERN CDS will trigger based on analytics of nursing documentation that indicates recognition and concern of patient changes. The CONCERN CDS will alert the care team of the patients "risky state" to increase team-based situational awareness (i.e., shared understanding of the patient situation) of patients predicted to be at risk for patient decompensating in need of rapid intervention to prevent mortality and associated harm.
Version 1: Burn in phase to evaluate adoption and adaptation to the algorithm being studied. Expected time frame - 3 months
Version 2: Version 2 refined based on continuous monitoring of data. Expected time frame - 3 months
Version 3: Version 3 refined based on continuous monitoring of data. Expected time frame - 3 months
| Study group | Role | What they receive |
|---|---|---|
| Control Group | No intervention | Control data will be collected in the CONCERN CDS system that will be "live" in the EHR, but in silent release mode (e.g., not providing notification to clinicians). |
| Intervention Group | Experimental | Experimental data will be collected in the CONCERN CDS system that will be "live" in the EHR in "active" release mode (e.g., providing CONCERN CDS system notification to clinicians). Receives: Behavioral: CONCERN CDS system notification |
What is being measured
The primary outcome is the main question the study is designed to answer.
In-hospital Mortalities
Number of patient deaths occurring in the hospital.
Measured: Up to 24 months
Average Length of Hospital Stay
The number of days that a patient was in the hospital
Measured: Up to 24 months
Secondary outcomes (4)
Number of Cardiac Arrests
Cardiopulmonary events during hospitalization
Measured: Up to 24 months
Number of Hospital Acquired Sepsis
Sepsis occurring during hospitalization
Measured: Up to 24 months
Number of Unanticipated Transfers to ICU
Transfer to ICU from acute care study units during hospitalization
Measured: Up to 24 months
Number of Hospital Readmissions
Readmission to the hospital within 30 days of being discharged alive.
Measured: Up to 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Apr 2019
- Study start21 Jul 2020
- Primary completion31 Oct 2022
- Study completion31 Oct 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (95)Papers the sponsor has linked to this study.
- Other trials for Hospital Acquired ConditionStudies of the same condition, open and under way.
- More trials run by Columbia UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Sequential
- Masking
- None (open label)
- Enrolment
- 60,893 participants (actual)
Sponsor and investigators
- Lead sponsor
- Columbia University
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute of Nursing Research (NINR), Brigham and Women's Hospital
- Responsible party
- Principal investigator, Sarah Collins Rossetti, Columbia University
- Investigators
- Patricia Dykes, PhD, Principal investigator, Brigham and Women's HospitalSarah Collins Rossetti, PhD, Principal investigator, Columbia UniversityKenrick Cato, PhD, Principal investigator, Columbia University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03911687
- Sponsor's protocol ID
- AAAR1389
- Other IDs
- R01NR016941
- Status last verified
- Apr 2025
- First submitted
- 28 Feb 2019
- Last updated
- 9 May 2025
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: This study will develop an open source SmartApp using interoperability standards. The investigators will make all source code available to any researchers upon request. This approach will ensure open access to all project aims, methods, resources, and deliverables by anyone via a study web-page. To aid in replication this web-page will include methods as well as other project processes such as stakeholder engagement, technical development, governance and lessons learned.
Publications
Hripcsak G, Zhou L, Parsons S, Das AK, Johnson SB. Modeling electronic discharge summaries as a simple temporal constraint satisfaction problem. J Am Med Inform Assoc. 2005 Jan-Feb;12(1):55-63. doi: 10.1197/jamia.M1623. Epub 2004 Oct 18.
PubMed 15492038Zhou L, Parsons S, Hripcsak G. The evaluation of a temporal reasoning system in processing clinical discharge summaries. J Am Med Inform Assoc. 2008 Jan-Feb;15(1):99-106. doi: 10.1197/jamia.M2467. Epub 2007 Oct 18.
PubMed 17947618Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS, Campbell E, Bates DW. Grand challenges in clinical decision support. J Biomed Inform. 2008 Apr;41(2):387-92. doi: 10.1016/j.jbi.2007.09.003. Epub 2007 Sep 21.
PubMed 18029232Hripcsak G, Elhadad N, Chen YH, Zhou L, Morrison FP. Using empiric semantic correlation to interpret temporal assertions in clinical texts. J Am Med Inform Assoc. 2009 Mar-Apr;16(2):220-7. doi: 10.1197/jamia.M3007. Epub 2008 Dec 11.
PubMed 19074297Muller-Engelmann M, Krones T, Keller H, Donner-Banzhoff N. Decision making preferences in the medical encounter--a factorial survey design. BMC Health Serv Res. 2008 Dec 17;8:260. doi: 10.1186/1472-6963-8-260.
PubMed 19091091Murphy SN, Weber G, Mendis M, Gainer V, Chueh HC, Churchill S, Kohane I. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med Inform Assoc. 2010 Mar-Apr;17(2):124-30. doi: 10.1136/jamia.2009.000893.
PubMed 20190053Albers DJ, Hripcsak G. A statistical dynamics approach to the study of human health data: resolving population scale diurnal variation in laboratory data. Phys Lett A. 2010 Feb 15;374(9):1159-1164. doi: 10.1016/j.physleta.2009.12.067.
PubMed 20544004Collins SA, Bakken S, Vawdrey DK, Coiera E, Currie L. Model development for EHR interdisciplinary information exchange of ICU common goals. Int J Med Inform. 2011 Aug;80(8):e141-9. doi: 10.1016/j.ijmedinf.2010.09.009. Epub 2010 Oct 25.
PubMed 20974549Collins SA, Bakken S, Vawdrey DK, Coiera E, Currie LM. Agreement between common goals discussed and documented in the ICU. J Am Med Inform Assoc. 2011 Jan-Feb;18(1):45-50. doi: 10.1136/jamia.2010.006437. Epub 2010 Nov 27.
PubMed 21113075Hripcsak G, Vawdrey DK, Fred MR, Bostwick SB. Use of electronic clinical documentation: time spent and team interactions. J Am Med Inform Assoc. 2011 Mar-Apr;18(2):112-7. doi: 10.1136/jamia.2010.008441. Epub 2011 Feb 2.
PubMed 21292706Senathirajah Y, Bakken S. Visual clustering analysis of CIS logs to inform creation of a user-configurable Web CIS interface. Methods Inf Med. 2011;50(4):337-48. doi: 10.3414/ME09-01-0087. Epub 2011 Jun 21.
PubMed 21691676Merchant RM, Yang L, Becker LB, Berg RA, Nadkarni V, Nichol G, Carr BG, Mitra N, Bradley SM, Abella BS, Groeneveld PW; American Heart Association Get With The Guidelines-Resuscitation Investigators. Incidence of treated cardiac arrest in hospitalized patients in the United States. Crit Care Med. 2011 Nov;39(11):2401-6. doi: 10.1097/CCM.0b013e3182257459.
PubMed 21705896Collins SA, Vawdrey DK. "Reading between the lines" of flow sheet data: nurses' optional documentation associated with cardiac arrest outcomes. Appl Nurs Res. 2012 Nov;25(4):251-7. doi: 10.1016/j.apnr.2011.06.002. Epub 2011 Nov 12.
PubMed 22079746Hripcsak G, Albers DJ, Perotte A. Exploiting time in electronic health record correlations. J Am Med Inform Assoc. 2011 Dec;18 Suppl 1(Suppl 1):i109-15. doi: 10.1136/amiajnl-2011-000463. Epub 2011 Nov 23.
PubMed 22116643Mandl KD, Mandel JC, Murphy SN, Bernstam EV, Ramoni RL, Kreda DA, McCoy JM, Adida B, Kohane IS. The SMART Platform: early experience enabling substitutable applications for electronic health records. J Am Med Inform Assoc. 2012 Jul-Aug;19(4):597-603. doi: 10.1136/amiajnl-2011-000622. Epub 2012 Mar 17.
PubMed 22427539
Study at a glance
From the registry record, last updated by the sponsor on 9 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Communicating Narrative Concerns Entered by RNs (CONCERN) Clinical Decision Support (CDS) System
- ClinicalTrials.gov ID
- NCT03911687
- Acronym / sponsor ID
- AAAR1389
- Conditions
- Hospital Acquired Condition
- Keywords
- Early warning system, Clinical decision support, Predictive analysis, Failure to rescue, Inter-professional situational awareness, Nursing surveillance, Healthcare process models of clinical concern (HPM-CC)
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 60,893 participants (actual)
- Lead sponsor
- Columbia UniversityUniversity, hospital or other
- Collaborators
- National Institute of Nursing Research (NINR), Brigham and Women's Hospital
- Study start
- 21 Jul 2020 (actual)
- Primary completion
- 31 Oct 2022 (actual)
- Study completion
- 31 Oct 2022 (actual)
- First posted
- 11 Apr 2019 (actual)
- Results
- Posted 9 May 2025
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
There are patients who die or have a bad outcome in the hospital and this could be prevented. Data in the nurses' notes could be used by computers to tell the rest of the care team that a patient is not doing well and that they should act more quickly. This project will build and evaluate a computer system that makes it easier for the care team to see and understand that data and act quickly to save patients. The aims of this study is to answer the questions, what is the level of provider use of the CONCERN CDS notification system (called CONCERN SMARTapp) and resulting impact on selected patient outcomes? Specifically, the study has 1) validated desired thresholds for the CONCERN CDS system and 2) integrated the CONCERN CDS system for early warning of risky patient states within CDS tools.
In this portion of the study (aim 3), the investigator will implement and evaluate the CONCERN CDS system on primary outcomes of in-hospital mortality and length of stay and secondary outcomes of cardiac arrest, unanticipated transfers to the intensive care unit, and 30-day hospital readmission rates.
Detailed description
Annually, more than 200,000 patients die in U.S. hospitals from cardiac arrest and over 130,000 patients inpatients deaths are attributed to sepsis. These deaths are preventable if patients who are at risk are detected earlier. Prior work found that nursing documentation within electronic health records (EHRs) contains information that could contribute to early detection and treatment, but these data are not being analyzed and exposed by EHRs to clinicians to initiate interventions quickly enough to save patients. A new source of predictive data is defined by analyzing the frequency and types of nursing documentation that indicated nurses' increased surveillance and level of concern for a patient. These data documented in the 48 hours preceding a cardiac arrest and hospital mortality were predictive of the event. While clinicians strive to provide the best care, there is a systematic problem within hospital settings of non-optimal communication between nurses and doctors leading to delays in care for patient at risk. Well-designed and tested EHRs are able to trend data and support communication and decision making, but too often fall short of these goals and actually increase clinician cognitive load through fragmented information displays, "note bloat", and information overload. Substitutable Medical Applications \& Reusable Technologies (SMARTapps) using Fast Health Interoperability Resource (FHIR) standard allow for open sharing and use of innovations across EHR systems. The aim of this project is to design and evaluate a SMARTapp on FHIR used across two large academic medical centers that exposes to physicians and nurses our new predictive data source from nursing documentation to increase care team situational awareness of at risk patients to decrease preventable adverse outcomes.
Communicating Narrative Concerns Entered by RNs (CONCERN) Clinical Decision Support (CDS) system is the application being designed and evaluated. CONCERN Intervention Trial Design will be a multiple time-series intervention. Baseline data will be collected at all study sites. Silent release mode (no SMARTapp notification) will be used in non-equivalent control units and as a post-intervention unit control to evaluate if notifying clinicians can decrease rates of length of stay on non-ICU units and rates of 30-day hospital readmissions. Different versions of the CDS system (SMARTapp) will be incorporated for dynamic, adaptive functionality and determine if the pattern of nursing documentation has changed. A "burn-in" phase is built in to evaluate adoption and adaptation to the algorithm and phases for deployment of the silent release mode within the multiple time-series intervention trial for a total of 18 months of data collection, including pre-intervention data collection and silent release modes.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Sequential
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Control Group | No intervention | –Control data will be collected in the CONCERN CDS system that will be "live" in the EHR, but in silent release mode (e.g., not providing notification to clinicians). |
| Intervention Group | Experimental | Behavioral: CONCERN CDS system notificationExperimental data will be collected in the CONCERN CDS system that will be "live" in the EHR in "active" release mode (e.g., providing CONCERN CDS system notification to clinicians). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| CONCERN CDS system notificationAlso: CONCERN SMARTapp notification, CONCERN Clinical Decision Support (CDS) system notificationThe CONCERN CDS will trigger based on analytics of nursing documentation that indicates recognition and concern of patient changes. The CONCERN CDS will alert the care team of the patients "risky state" to increase team-based situational awareness (i.e., shared understanding of the patient situation) of patients predicted to be at risk for patient decompensating in need of rapid intervention to prevent mortality and associated harm. Version 1: Burn in phase to evaluate adoption and adaptation to the algorithm being studied. Expected time frame - 3 months Version 2: Version 2 refined based on continuous monitoring of data. Expected time frame - 3 months Version 3: Version 3 refined based on continuous monitoring of data. Expected time frame - 3 months | Behavioural | Intervention Group |
Primary outcome
In-hospital Mortalities
Time frame: Up to 24 months
Number of patient deaths occurring in the hospital.
Average Length of Hospital Stay
Time frame: Up to 24 months
The number of days that a patient was in the hospital
4 secondary outcomes
Number of Cardiac Arrests
Time frame: Up to 24 months
Number of Hospital Acquired Sepsis
Time frame: Up to 24 months
Number of Unanticipated Transfers to ICU
Time frame: Up to 24 months
Number of Hospital Readmissions
Time frame: Up to 24 months
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 May 2025.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Background: PubMed 15492038Hripcsak G, Zhou L, Parsons S, Das AK, Johnson SB. Modeling electronic discharge summaries as a simple temporal constraint satisfaction problem. J Am Med Inform Assoc. 2005 Jan-Feb;12(1):55-63. doi: 10.1197/jamia.M1623. Epub 2004 Oct 18.
- Background: PubMed 17947618Zhou L, Parsons S, Hripcsak G. The evaluation of a temporal reasoning system in processing clinical discharge summaries. J Am Med Inform Assoc. 2008 Jan-Feb;15(1):99-106. doi: 10.1197/jamia.M2467. Epub 2007 Oct 18.
- Background: PubMed 18029232Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS, Campbell E, Bates DW. Grand challenges in clinical decision support. J Biomed Inform. 2008 Apr;41(2):387-92. doi: 10.1016/j.jbi.2007.09.003. Epub 2007 Sep 21.
- Background: PubMed 19074297Hripcsak G, Elhadad N, Chen YH, Zhou L, Morrison FP. Using empiric semantic correlation to interpret temporal assertions in clinical texts. J Am Med Inform Assoc. 2009 Mar-Apr;16(2):220-7. doi: 10.1197/jamia.M3007. Epub 2008 Dec 11.
- Background: PubMed 19091091Muller-Engelmann M, Krones T, Keller H, Donner-Banzhoff N. Decision making preferences in the medical encounter--a factorial survey design. BMC Health Serv Res. 2008 Dec 17;8:260. doi: 10.1186/1472-6963-8-260.
- Background: PubMed 20190053Murphy SN, Weber G, Mendis M, Gainer V, Chueh HC, Churchill S, Kohane I. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med Inform Assoc. 2010 Mar-Apr;17(2):124-30. doi: 10.1136/jamia.2009.000893.
- Background: PubMed 20544004Albers DJ, Hripcsak G. A statistical dynamics approach to the study of human health data: resolving population scale diurnal variation in laboratory data. Phys Lett A. 2010 Feb 15;374(9):1159-1164. doi: 10.1016/j.physleta.2009.12.067.
- Background: PubMed 20974549Collins SA, Bakken S, Vawdrey DK, Coiera E, Currie L. Model development for EHR interdisciplinary information exchange of ICU common goals. Int J Med Inform. 2011 Aug;80(8):e141-9. doi: 10.1016/j.ijmedinf.2010.09.009. Epub 2010 Oct 25.
- Background: PubMed 21113075Collins SA, Bakken S, Vawdrey DK, Coiera E, Currie LM. Agreement between common goals discussed and documented in the ICU. J Am Med Inform Assoc. 2011 Jan-Feb;18(1):45-50. doi: 10.1136/jamia.2010.006437. Epub 2010 Nov 27.
- Background: PubMed 21292706Hripcsak G, Vawdrey DK, Fred MR, Bostwick SB. Use of electronic clinical documentation: time spent and team interactions. J Am Med Inform Assoc. 2011 Mar-Apr;18(2):112-7. doi: 10.1136/jamia.2010.008441. Epub 2011 Feb 2.
- Background: PubMed 21691676Senathirajah Y, Bakken S. Visual clustering analysis of CIS logs to inform creation of a user-configurable Web CIS interface. Methods Inf Med. 2011;50(4):337-48. doi: 10.3414/ME09-01-0087. Epub 2011 Jun 21.
- Background: PubMed 21705896Merchant RM, Yang L, Becker LB, Berg RA, Nadkarni V, Nichol G, Carr BG, Mitra N, Bradley SM, Abella BS, Groeneveld PW; American Heart Association Get With The Guidelines-Resuscitation Investigators. Incidence of treated cardiac arrest in hospitalized patients in the United States. Crit Care Med. 2011 Nov;39(11):2401-6. doi: 10.1097/CCM.0b013e3182257459.
- Individual participant dataSharing planned: No. This study will develop an open source SmartApp using interoperability standards. The investigators will make all source code available to any researchers upon request. This approach will ensure open access to all project aims, methods, resources, and deliverables by anyone via a study web-page. To aid in replication this web-page will include methods as well as other project processes such as stakeholder engagement, technical development, governance and lessons learned.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Patricia Dykes, PhD principal investigator
Brigham and Women's Hospital
Sarah Collins Rossetti, PhD principal investigator
Columbia University
Kenrick Cato, PhD principal investigator
Columbia University
4 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing CONCERN CDS system notification in people with Hospital Acquired Condition, run by Columbia University at 4 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 9 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 9 May 2025). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
In-hospital Mortalities (time frame: Up to 24 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03911687, which also lists 95 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 May 2025; AzTrials last refreshed its copy on 28 Oct 2026.