Template for Draft Reports



146BEvidence Tables for Chapter 36. Monitoring Patient Safety Problems (NEW)The layout of this evidence table is customized based on the data reported by the included studies. Some columns in the evidence tables for other PSP topics are not included in this table or merged with other columns. For example, the “Description of Organization” column is merged into the “Context” column. There is no “Theory or Logic Model” column in this table because none of the included studies reported such data. The customized layout allows the data collected to fit into the table appropriately.Table 1, Chapter 36. Evidence from studies comparing methods for detecting patient safety problemsAuthor/ YearDescription of PSPStudy DesignContextsOutcomes: Benefits or HarmsOlsen 2007 ADDIN REFMGR.CITE <Refman><Cite><Author>Olsen</Author><Year>2007</Year><RecNum>583454</RecNum><IDText>Hospital staff should use more than one method to detect adverse events and potential adverse events: incident reporting, pharmacist surveillance and local real-time record review may all have a place</IDText><MDL Ref_Type="Journal"><Ref_Type>Journal</Ref_Type><Ref_ID>583454</Ref_ID><Title_Primary>Hospital staff should use more than one method to detect adverse events and potential adverse events: incident reporting, pharmacist surveillance and local real-time record review may all have a place</Title_Primary><Authors_Primary>Olsen,S.</Authors_Primary><Authors_Primary>Neale,G.</Authors_Primary><Authors_Primary>Schwab,K.</Authors_Primary><Authors_Primary>Psaila,B.</Authors_Primary><Authors_Primary>Patel,T.</Authors_Primary><Authors_Primary>Chapman,E.J.</Authors_Primary><Authors_Primary>Vincent,C.</Authors_Primary><Date_Primary>2007/2</Date_Primary><Keywords>Adverse Drug Reaction Reporting Systems</Keywords><Keywords>*statistics &amp; numerical data</Keywords><Keywords>Data Collection</Keywords><Keywords>Female</Keywords><Keywords>Great Britain</Keywords><Keywords>Health Care Surveys</Keywords><Keywords>Hospitals,District</Keywords><Keywords>Hospitals,General</Keywords><Keywords>Humans</Keywords><Keywords>Male</Keywords><Keywords>*Medical Records</Keywords><Keywords>Medical Staff,Hospital</Keywords><Keywords>Medication Errors</Keywords><Keywords>statistics &amp; numeri</Keywords><Reprint>Not in File</Reprint><Start_Page>40</Start_Page><End_Page>44</End_Page><Periodical>Qual Saf Health Care</Periodical><Volume>16</Volume><Issue>1</Issue><User_Def_2>MEDLINE - Ovid 11/18/2011</User_Def_2><User_Def_3>Given to Fang Sun on 11/18/2011 for EPC0019</User_Def_3><ISSN_ISBN>17301203</ISSN_ISBN><Availability>Sharepoint , EPC0019 , EPC19_Final_12-9-11</Availability><Address>Clinical Safety Research Unit, Department of Bio-surgery and Technology, Imperial College, St Mary&apos;s Hospital, London, UK. s.olsen@imperial.ac.uk</Address><ZZ_JournalStdAbbrev><f name="System">Qual Saf Health Care</f></ZZ_JournalStdAbbrev><ZZ_WorkformID>1</ZZ_WorkformID></MDL></Cite></Refman>1Three methods for detecting adverse events (AEs) were studied:1)Incident reportsAt the time of data collection risk managers encouraged reporting of AEs and near misses but provided no further criteria or guidelines for reporting except that it was mandatory to supply details of incidents in which security staff are involved. Reporting is confidential but not anonymous. The forms contain both mandatory data fields and space for free text. During the periods of data collection there were neither additional incentives nor specific encouragements to enhance reporting.2)Active surveillance of prescription charts by pharmacistsHospital pharmacists attend the wards on weekdays during normal working hours to ensure continuity of pre-admission medications and to detect prescribing errors. After discussion with ward doctors errors and omissions are corrected on the prescription charts. For each intervention a brief record is made on a standardized form. The forms related to the care of the 288 patients entered into the study were collected and analyzed centrally in the pharmacy.3)Record review at time of dischargeSpecialist registrars (senior residents) monitored by external reviewers assessed all case records within 10 days of discharge of consecutively discharged or deceased patients from the participating firms. The occurrence of an adverse event or potential adverse event was determined for each case. Each?event was classified according to the stage of care and a mutually exclusive problem category (diagnosis, overall assessment of patient’s condition including comorbidities, technical problems occurring during a procedure, infection-related, general problems with ongoing monitoring and management of patients and medication-related problems). Record review was also carried out by members of the clinical team caring for the patients. But in this report only the data collected by the external assessors were used.This is a prospective observational study. Data on AEs were collected on 288?patients discharged from adult medical and surgical units in an acute care hospitalExternal: In the UK, several initiatives had been established by the Department of Health to promote patient safety. The National Reporting and Learning System (NRLS) established by the National Patient Safety Agency (NPSA) is one of the initiatives. Incidents reported to nine types of the National Health System (NHS) Trust (ranging from acute general hospitals to community optometry), are relayed centrally for classification and anizational Characteristics: A district general acute care hospital in the NHS in the UK. The hospital had an 850-bed and received around 40 000 admissions per year. The hospital trust covers a full range of medical and general surgical specialties backed up by full intensive care facilities. Teamwork: Safety data were collected from three general medical and three general surgical teams. The teams were selected by the head of risk management.Leadership: None mentionedCulture: “In this hospital, as throughout the NHS, risk managers encourage clinical staff to report, on printed forms, incidents that may affect patients adversely.”Implementation tools: Various forms were used for data collection.Record review detected 26 (9%) AEs and 40 (14%) potential adverse events (PAEs) occurring during the index admission. Three adverse events and 11 potential adverse events were associated with medications. Other commonly occurring events included inadequate clinical monitoring and management (17/66), technical problems with a procedure (9/66), infection-related problems (8/66) and failure to arrange adequate follow-up or care at discharge (7/66).Incident reporting detected 11?PAEs and no AEs. These PAEs included delay in cross-matching blood for a patient requiring surgery; poor clinical hand over of a patient from accident and emergency to ward staff; a fall causing a bruised head that required medical assessment, an intravenous cannula misplaced in the brachial artery, five concerned falls without significant injury and two episodes in which security staff were called in relation to absconded or aggressive patients.Pharmacy surveillance found 30?medication errors all of which were PAEs. The most common problems related to failure to prescribe regular or indicated medication (15/30) and failure to prescribe the correct dose of a drug (9/30).There was little overlap in the nature of events detected by the three methods.Wetzels 2008 ADDIN REFMGR.CITE <Refman><Cite><Author>Wetzels</Author><Year>2008</Year><RecNum>578345</RecNum><IDText>Mix of methods is needed to identify adverse events in general practice: a prospective observational study</IDText><MDL Ref_Type="Journal"><Ref_Type>Journal</Ref_Type><Ref_ID>578345</Ref_ID><Title_Primary>Mix of methods is needed to identify adverse events in general practice: a prospective observational study</Title_Primary><Authors_Primary>Wetzels,R.</Authors_Primary><Authors_Primary>Wolters,R.</Authors_Primary><Authors_Primary>van,Weel C.</Authors_Primary><Authors_Primary>Wensing,M.</Authors_Primary><Date_Primary>2008</Date_Primary><Keywords>Adolescent</Keywords><Keywords>Adult</Keywords><Keywords>Aged</Keywords><Keywords>Aged,80 and over</Keywords><Keywords>Child</Keywords><Keywords>Child,Preschool</Keywords><Keywords>Death Certificates</Keywords><Keywords>Family Practice</Keywords><Keywords>*standards</Keywords><Keywords>Female</Keywords><Keywords>Humans</Keywords><Keywords>Male</Keywords><Keywords>Medical Audit</Keywords><Keywords>Medical Errors</Keywords><Keywords>*statistics &amp; numerical data</Keywords><Keywords>Medical Records</Keywords><Keywords>Medication Errors</Keywords><Keywords>statistics &amp; numerical data</Keywords><Keywords>Middle</Keywords><Reprint>Not in File</Reprint><Start_Page>35</Start_Page><Periodical>BMC Fam Pract</Periodical><Volume>9</Volume><User_Def_2>MEDLINE - Ovid 11/10/2011, MEDLINE - Ovid 9/13/2011</User_Def_2><User_Def_3>Given to Fang Sun on 9/26/2011 for EPC0019</User_Def_3><ISSN_ISBN>18554418</ISSN_ISBN><Availability>Sharepoint , EPC0019 , SRPMEPC19_091311 , EPC19_Final_12-9-11</Availability><Address>Radboud University Nijmegen Medical Centre, Centre for Quality of Care Research, P,O, Box 9101, 6500 HB Nijmegen, The Netherlands. M.Wensing@kwazo.umcn.nl</Address><ZZ_JournalStdAbbrev><f name="System">BMC Fam Pract</f></ZZ_JournalStdAbbrev><ZZ_WorkformID>1</ZZ_WorkformID></MDL></Cite></Refman>2Five methods for identifying adverse events in general practice:Physician reported adverse eventsThe physicians recorded all events using a simplified computerized registration form based on an existing international taxonomy for errors in general practice. The physicians registered event date, birth date of patient, gender, event category (practice administration (archive; medical record; appointment; other), diagnostic (wrong diagnosis; delayed diagnosis; missed diagnosis; other), therapeutic (wrong, incomplete; delayed; none, though it should be; other), communication (with patients; with caregivers; other), and additional remarks and/or context.Pharmacist reported adverse eventsThe pharmacist recorded events from her point of view using an adjusted form developed for this purpose. Event date, birth date of patient, gender, practice, event category (prescribing error (wrong prescription, wrong administration; wrong dose; other), adverse reaction (adverse reaction; allergic reaction; overdose; interaction; contra-indication; other), dispensing error (too late; wrong medicine; wrong dose; other), and additional remarks or context were recorded.3)Patients’ experiences of adverse eventsIn the waiting room of the two practices samples of 50 patients, consecutively visiting the practice, were invited to complete a questionnaire on experienced problems with safety of their health care in the previous six months. A drop box was used to collect the completed questionnaires. Questions were derived from items of the Medical Harvard Study, and from questions of two survey studies. The questionnaire guaranteed anonymity of participating patients.4)Assessment of a random sample of medical recordsThirty medical records per physician were randomly selected from patients who had visited their general practice in the observation period. Anonymous medical records, containing the information from this period were printed out. Two clinical researchers examined the information independently. They scrutinized the records for indications of events and, when found, categorized the event (errors in office administration, diagnosis, treatment or communication with their subcategories); and added demographic data of the patient. Subsequently the physicians discussed their findings and reached consensus.5)Assessment of all deceased patientsOne physician examined the medical records of the patients who had died in the period of the study for events. The same registration form and analysis procedure as for the audits of medical records was used.A prospective observational study, comparing the five methods in two general practices in a period of five months (May to October 2006)A total of approximately 8,250?patients were registered with the two practicesExternal: None mentionedOrganizational Characteristics: Two general practices in the Netherlands; no other detail providedTeamwork: Multiple physicians, pharmacists, or researchers were involved in the study collecting or reviewing data. Leadership: None mentionedCulture: None mentionedImplementation tools: The physicians recorded all events using a simplified computerized registration form based on an existing international taxonomy for errors in general practice. The pharmacist recorded events from her point of view using an adjusted form developed for this purpose. A questionnaire was used to collect data patients’ experience. Refer to “Description of PSP” for more description.A total of 68 events were identified using these methods. The events detected in four categories: 1) Events in office administration, 2) Events in diagnosis, 3) Treatment events, and 4) Events in Communication. All five methods proved to identify a number of adverse events. Each of the methods provided events that were not found with other methods. There was no overlap between the methods regarding the identified events. The patient survey accounted for the highest number of events and the pharmacist reports for the lowest number. All methods resulted in a variety of events, except for the pharmacist reports, which only referred to pharmaceutical treatment. The identified events referred to adult male and female patients of all ages, but events on children were very seldom reported.Ferranti 2008 ADDIN REFMGR.CITE <Refman><Cite><Author>Ferranti</Author><Year>2008</Year><RecNum>582822</RecNum><IDText>A multifaceted approach to safety: the synergistic detection of adverse drug events in adult inpatients</IDText><MDL Ref_Type="Journal"><Ref_Type>Journal</Ref_Type><Ref_ID>582822</Ref_ID><Title_Primary>A multifaceted approach to safety: the synergistic detection of adverse drug events in adult inpatients</Title_Primary><Authors_Primary>Ferranti,J.</Authors_Primary><Authors_Primary>Horvath,M.M.</Authors_Primary><Authors_Primary>Cozart,H.</Authors_Primary><Authors_Primary>Whitehurst,J.</Authors_Primary><Authors_Primary>Eckstrand,J.</Authors_Primary><Authors_Primary>Pietrobon,R.</Authors_Primary><Authors_Primary>Rajgor,D.</Authors_Primary><Authors_Primary>Ahmad,A.</Authors_Primary><Date_Primary>2008</Date_Primary><Reprint>Not in File</Reprint><Start_Page>184</Start_Page><End_Page>190</End_Page><Periodical>J Patient Saf</Periodical><Volume>4</Volume><User_Def_2>Hand Entry 11/10/2011</User_Def_2><User_Def_3>Given to Fang Sun on 11/10/2011 for EPC0019</User_Def_3><Availability>Sharepoint , EPC0019 , EPC19_Final_12-9-11</Availability><ZZ_JournalStdAbbrev><f name="System">J Patient Saf</f></ZZ_JournalStdAbbrev><ZZ_WorkformID>1</ZZ_WorkformID></MDL></Cite></Refman>3Two ADE detection systems were studied:1)Voluntary reportingThe safety reporting system was developed as a home-grown web application to provide a single point of entry for voluntary reporting and allow standardized evaluation of safety events across Duke University Health System (DUHS). All DUHS employees may access the reporting system and are encouraged to report any safety events witnessed, including near misses. Although anonymous reporting is possible, DUHS policy supports a non-punitive culture of safety. Safety reporting system captures a myriad of event types including medication/intravenous-related, blood transfusions, surgical, falls, treatment/testing, dissatisfied patient, and others. Each medication/intravenous-related report was investigated by a team of 4 medication safety pharmacists and scored for severity before submission to a multidisciplinary leadership team for review and confirmation. All events with a severity score were deemed adverse drug events (ADEs).2)Computerized surveillanceThe DUH’s computerized ADE-S system was deployed by an internal team of technical and safety experts. Each evening, ADE-S evaluates medication, laboratory, and patient demographic information against a set of clinical rules or triggers to detect potential ADEs or evolving unsafe conditions. Nearly 130 rules have been deployed since the system’s inception, but only 14 high-risk rules with high true-positive rates were considered in surveillance. These 14 rules span 3 main categories: abnormal laboratory results, use of antidotes, and drug-lab combinations. Adverse drug event surveillance delivers an electronic daily report to a web-based surveillance application that details all triggers fired by the system. This list was evaluated by 3 clinical pharmacists who perform a chart review to determine whether an ADE occurred. Pharmacists identified all possible medications involved in the event and assigned a causality score using the Naranjo algorithm and a severity score using the DUH 7-point scale. All events scored with causality Q5 and a severity Q3 were considered ADEs. Pair wise inter-rater reliability scores (J statistic) exceeded 0.88 for each rater pair.The study retrospectively analyzed all ADEs detected using the two independent system in adults treated in the hospital (all inpatients receiving service on 23?adult care nursing units between December 1st, 2006 and June 30th, 2007).Adult, inpatient ADEs were evaluated and scored using a standardized methodology. ADEs per 1,000 patient days were calculated.External: None mentionedOrganizational Characteristics: It is a large, tertiary care academic medical center in the DUHSTeamwork: For both voluntary reporting and computerized surveillance, multidisciplinary teams were used for investigating reviewing and confirming the findings. Refer to “Description of PSP” for more detail.Leadership: For voluntary reporting, a multidisciplinary leadership team reviewed and confirmed the findings.Culture: There is “a highly vigilant, non-punitive culture of safety at DUH”Implementation tools: Business intelligence software was used to provide real time access to event reports from both the For both voluntary reporting and computerized surveillance systems to empower caregivers with safety data originating from their clinical care puterized surveillance detected 710 ADEs (6.93/1,000 patient days), whereas voluntary reporting identified 205 ADEs (1.96/1,000 patient days). For each major drug category (anticoagulants, hypoglycemia, narcotics and benzodiazepines, and miscellaneous), surveillance and voluntary reporting detected significantly different event rates. Most surveillance events were hypoglycemia-related, whereas most voluntarily-reported events were in the miscellaneous category. The 2 systems detected statistically different ADE rates when stratified by nursing station. Of all unique ADEs (875), only 40?(5.6%) were common between the systems.Levinson 2010 ADDIN REFMGR.CITE <Refman><Cite><Year>2010</Year><RecNum>589232</RecNum><IDText>Adverse events in hospitals: methods for indentifying events [OEI-06-08-00221]</IDText><MDL Ref_Type="Journal"><Ref_Type>Journal</Ref_Type><Ref_ID>589232</Ref_ID><Title_Primary>Adverse events in hospitals: methods for indentifying events [OEI-06-08-00221]</Title_Primary><Date_Primary>2010/3</Date_Primary><Reprint>Not in File</Reprint><Start_Page>60</Start_Page><Pub_Place>Washington (DC)</Pub_Place><Publisher>Department of Health and Human Services, Office of Inspector General</Publisher><User_Def_2>Hand Entry 2/9/2012</User_Def_2><User_Def_3>Given to Fang Sun on 2/9/2012 for EPC0019</User_Def_3><Availability>Sharepoint , EPC0019 , EPC19_4Adds_2-13-12</Availability><ZZ_WorkformID>1</ZZ_WorkformID></MDL></Cite></Refman>4The following safety problem monitoring methods were assessed:1) Nurse ReviewsContracted registered nurses reviewed medical records for each sampled 278 Medicare beneficiary’s hospitalization. Nurses used a standardized review process developed by the Institute for Healthcare Improvement (IHI) as part of its Global Trigger Tool (GTT) protocol. The nurse review used IHI’s GTT worksheet that listed 54 “triggers” that could be found within a medical record to indicate the possibility of an event. When a trigger was found, the nurse reviewer explored the medical record further to identify possible events and associated level of harm. 2) Analysis of present-on-admission (POA) IndicatorsAdministrative billing data directly from hospitals for each of the 278 sample Medicare beneficiary hospitalizations was analyzed. POA indicators in the billing data was used to identify hospitalizations that may have had events. When the POA indicator showed that a diagnosis was not present upon admission, the investigator concluded that the condition developed during the hospital stay and might have been the result of an event.3) Beneficiary InterviewsThe investigators conducted telephone interviews with 220 of the 278 Medicare beneficiaries or their family members to learn about the medical care experienced during sampled hospitalizations. The interview protocol was designed to determine whether beneficiaries experienced any episodes while in the hospital that might have involved events. It also included questions about such topics as medications, procedures, infections, and falls.4) Hospital Incident ReportsThe investigators requested that hospitals provide any internal incident reports, such as submissions to any hospital incident-reporting systems, adverse drug reaction reports, complaints, peer reviews, and mortality and morbidity reviews associated with the 278 sample Medicare beneficiary hospitalizations. Reports provided by hospitals included issues related to risk management, hospital infections, surgical management, and others.5) Analysis of Patient Safety IndicatorsThe investigators applied the Agency for Healthcare Research and Quality’s (AHRQ’s) Patient Safety Indicator (PSI) software program to hospital administrative billing data for the 278 sample Medicare beneficiary hospitalizations. AHRQ developed the PSI software to monitor health care quality using administrative data, such as patient demographics (e.g., age, gender), and diagnoses and procedure codes. The PSI software is based upon a series of algorithms that detect 20 provider-level complications that indicate possible events (e.g., death of a low-risk patient).The study retrospectively evaluated the usefulness of five methods for identifying patient safety events in 278 Medicare beneficiary hospitalizations selected from all Medicare discharges from acute care hospitals in two selected counties during a 1-week period in August 2008. The investigators compared events flagged by each method to the 120 events identified and/or confirmed through physician reviews. External: None mentionedOrganizational Characteristics: None mentionedTeamwork: None mentionedLeadership: None mentionedCulture: None mentionedImplementation tools: IHI’s GTT worksheet, POA indicators, and AHRQ’s PSI software program were used in the study. Detailed description of these tools was provided in the appendix of the Levinson study. ADDIN REFMGR.CITE <Refman><Cite><Year>2010</Year><RecNum>589232</RecNum><IDText>Adverse events in hospitals: methods for indentifying events [OEI-06-08-00221]</IDText><MDL Ref_Type="Journal"><Ref_Type>Journal</Ref_Type><Ref_ID>589232</Ref_ID><Title_Primary>Adverse events in hospitals: methods for indentifying events [OEI-06-08-00221]</Title_Primary><Date_Primary>2010/3</Date_Primary><Reprint>Not in File</Reprint><Start_Page>60</Start_Page><Pub_Place>Washington (DC)</Pub_Place><Publisher>Department of Health and Human Services, Office of Inspector General</Publisher><User_Def_2>Hand Entry 2/9/2012</User_Def_2><User_Def_3>Given to Fang Sun on 2/9/2012 for EPC0019</User_Def_3><Availability>Sharepoint , EPC0019 , EPC19_4Adds_2-13-12</Availability><ZZ_WorkformID>1</ZZ_WorkformID></MDL></Cite></Refman>4Nurse reviews and POA analysis identified the greatest number of safety events. Nurse reviews identified 93 of the 120 safety events in the case study and POA analysis identified 61 events. Beneficiary interviews identified 22 events, and the remaining 2 screening methods identified 8 events each. Of the 120 events, 55 (46 percent) were identified by only 1 screening method. Nurse reviews identified 35 events (29?percent of the 120 events) not?flagged by any other screening method. POA analysis alone flagged 14 events (12?percent of the 120 events). Although the five screening methods were useful in identifying events, 406 of the 662 flags generated by the methods were not associated with any of the 120?events identified in the case study. The POA analysis generated the most flags that were not associated with events (183 flags) and PSI analysis generated the fewest (4 flags). Levtzion-Korach 2010 ADDIN REFMGR.CITE <Refman><Cite><Author>Van Der</Author><Year>2011</Year><RecNum>582729</RecNum><IDText>National register study of operating time and outcome in hernia repair</IDText><MDL Ref_Type="Journal"><Ref_Type>Journal</Ref_Type><Ref_ID>582729</Ref_ID><Title_Primary>National register study of operating time and outcome in hernia repair</Title_Primary><Authors_Primary>Van Der,Linden W.</Authors_Primary><Authors_Primary>Warg,A.</Authors_Primary><Authors_Primary>Nordin,P.</Authors_Primary><Date_Primary>2011/10</Date_Primary><Keywords>adult</Keywords><Keywords>aged</Keywords><Keywords>article</Keywords><Keywords>female</Keywords><Keywords>hematoma</Keywords><Keywords>co [Complication]</Keywords><Keywords>*hernioplasty</Keywords><Keywords>human</Keywords><Keywords>inguinal hernia</Keywords><Keywords>su [Surgery]</Keywords><Keywords>laparoscopic surgery</Keywords><Keywords>major clinical study</Keywords><Keywords>male</Keywords><Keywords>observational study</Keywords><Keywords>*operation duration</Keywords><Keywords>orchitis</Keywords><Keywords>co [Complication]</Keywords><Keywords>population recovery</Keywords><Keywords>postoperative infec</Keywords><Reprint>Not in File</Reprint><Start_Page>1198</Start_Page><End_Page>1203</End_Page><Periodical>Arch Surg</Periodical><Volume>146</Volume><Issue>10</Issue><User_Def_2>MEDLINE - Ovid 11/17/2011, EMBASE (OVID) 11/4/2011</User_Def_2><User_Def_3>Given to Jon Treadwell on 11/15/2011 for EPC0014</User_Def_3><ISSN_ISBN>22006880</ISSN_ISBN><Availability>Sharepoint , EPC0014 , SREMEPC14_1014-1104 , SRPMEPC14_111711 , EPC14_41Adds_12-2-11, EPC19_Final_12-9-11</Availability><Address>(Van Der Linden, Warg, Nordin) Department of Surgery, Ostersund Hospital, S 831 83 Ostersund, Sweden (Nordin) Department of Surgical and Perioperative Sciences, Umea University, Umea, Sweden (Nordin) Swedish Hernia Register, Sweden</Address><ZZ_JournalStdAbbrev><f name="System">Arch Surg</f></ZZ_JournalStdAbbrev><ZZ_WorkformID>1</ZZ_WorkformID></MDL></Cite></Refman>5The following safety problem monitoring methods were assessed: 1)An incident reporting systemThe hospital used a commercially available Web-based incident reporting system. Hospital personnel could report confidentially through any hospital computer using a secure login and could report anything that they perceive might be an issue. Each adverse event report contains the reporter’s initial comments and a section for the departmental manager to clarify issues further and add comments and actions. The manager is responsible for reviewing each report and assigning one or more contributing factors from a drop-down list of 50 potential contributing factors. For the most important reports, management will have direct conversation with the reporters after the evaluation is complete2)Reports to hospital risk managementA nurse-lawyer leads the risk management team. Physicians and nurses, in about equal numbers, call the team to report adverse events and poor patient outcomes. Risk management staff members investigate each case and determine on the basis of the estimated risk whether to report the case to the malpractice carrier. This information is collected manually with no systematic categorization and is entered in an electronic index. Risk management also provides information back to managers or frontline individuals so that risks can be mitigated.3)A patient complaints databaseThe hospital’s Family and Patient Relations Department responds to patient and family complaints (concerns), suggestions, and compliments. The department’s coordinators receive the complaints, assign them to one of 20?categories and one or more of 118?subcategories, and process them into a database. The department works directly with the hospital risk management team and safety team, which includes a physician, nurses, and safety analysts; although the analysts mostly do not have a medical background, they are trained in patient safety.4)Executive walk roundsExecutive leadership walk rounds began at the hospital in January 2001. Semiweekly, a member of the hospital leadership (hospital chief executive officer, chief medical officer, chief nursing officer, chief operating officer) accompanied by the hospital’s safety officer, a safety analyst, and a pharmacy representative visits a different service in the hospital and engages with the staff (mainly nurses but occasionally also physicians) about safety concerns. In stimulated discussions, staff is encouraged to speak freely and make suggestions for improvement. The staff comments (negative and positive) are assigned one or more (out of 51) contributing factors and a priority score, which are then recorded in an electronic database. Analyses of the comments are then compiled into action items that are discussed with the accountable vice president.5)Malpractice claimsThe hospital used a data collection system called CMAPS (Claims Management, Analysis, and Processing System) from the malpractice insurer, CRICO/Risk Management Foundation (RMF; Cambridge, MA). Initial information is obtained from potential claim reports, hospital risk managers, or from formal malpractice claims and suits. Further information is added as it becomes available (for example, depositions, expert reviews, medical records, adjustor notes). Nurse coders assign one or more (from 170) risk management issues, factors that may have contributed to the allegation, injury, or initiation of the claim/suit. There are clear definitions, standardized coding algorithms, and collaboration between coders leading to high inter-rater reliability. The data are stored in an electronic database that is available for querying, analysis, and generation of reports. There are about 30?claims per year.This is a prospective observational study, comparing the five safety problem detection methods. Data were collected for a 22-month period from May?10,?2004, to February?28,?2006.8,616 incident reports (involving 13,255 contributing factors), 1,003?risk management reports, 4,722 patient complaints (involving 6,617 specific problems), 61 walk rounds (involving 572 comments), and 322 malpractice claims (involving 949 issues) were evaluated.External: None mentionedOrganizational Characteristics: This study was performed at Brigham and Women’s Hospital, a?747-bed tertiary care academic medical center affiliated with Harvard Medical School. There?are approximately 52,000?inpatient admissions and 950,000 outpatient visits annually. The hospital employs more than 12,000 people, of whom approximately 3,000 are physicians.The hospital “had more independent data sources than is the norm.”Teamwork: The study mentioned multidisciplinary team efforts for some methods used (e.g.,?hospital risk management, handling patient complaints databases, and executive walk rounds). Refer to “Description of PSP” for more description.Leadership: For executive walk round, the deep involvement by the top-level executives was mentioned in the study. Refer to “Description of PSP” for more description.Culture: The institution has “a?history of patient safety awareness,” and was “willing to allow all its defect data to be closely examined.”Implementation tools: The hospital used a commercially available Web-based incident reporting system. The hospital used CMAPS to collect data on malpractice claims. Refer to “Description of PSP” for more description about the incident reporting system and the CMAPS system.Across the five methods, the leading categories of safety problems were communication, 11.6%; technical skills, 10.9%; and clinical judgment, 9%. Each of the methods had a different category that was most frequent.Clinical judgment was the leading category in the malpractice claims data (24.3%) but was barely represented in the incident reporting system (1.1%) and not represented at all in executive walk munication played an important role both in the malpractice claims (17.1%) and the patient complaints data (21.8%) but not in the hospital’s risk management data (3%).Provider behavior accounted for 19% of complaints in the patient complaints system, second only to communication (clearly the two are closely related). However, provider behavior represented only 1.1% of the malpractice claims and 2.1% of reports to risk?management and was not represented in the executive walk rounds or incident reporting system.Equipment (15.7%), electronic?records (12.2%), and environment/infrastructure (12.1%) were the leading categories in executive walk rounds but were ranked low in the other systems. In the incident reports, identification issues (24.4%) and falls (16.8%) were the leading categories but were barely represented in the other systems.Overall, there is a low level of consistency across the five methods. The highest correlations between the different categories across the methods were between malpractice claims, reports to risk management, and patient complaints. The adverse event reporting system and executive walk rounds had low and negative correlation with the other four systems.Tinoco 2011 ADDIN REFMGR.CITE <Refman><Cite><Author>Tinoco</Author><Year>2011</Year><RecNum>578365</RecNum><IDText>Comparison of computerized surveillance and manual chart review for adverse events</IDText><MDL Ref_Type="Journal"><Ref_Type>Journal</Ref_Type><Ref_ID>578365</Ref_ID><Title_Primary>Comparison of computerized surveillance and manual chart review for adverse events</Title_Primary><Authors_Primary>Tinoco,A.</Authors_Primary><Authors_Primary>Evans,R.S.</Authors_Primary><Authors_Primary>Staes,C.J.</Authors_Primary><Authors_Primary>Lloyd,J.F.</Authors_Primary><Authors_Primary>Rothschild,J.M.</Authors_Primary><Authors_Primary>Haug,P.J.</Authors_Primary><Date_Primary>2011/7</Date_Primary><Keywords>Cross Infection</Keywords><Keywords>*prevention &amp; control</Keywords><Keywords>Humans</Keywords><Keywords>Medical Audit</Keywords><Keywords>*methods</Keywords><Keywords>Medication Errors</Keywords><Keywords>*prevention &amp; control</Keywords><Keywords>*Natural Language Processing</Keywords><Keywords>Population Surveillance</Keywords><Keywords>*methods</Keywords><Keywords>Retrospective Studies</Keywords><Keywords>Risk Management</Keywords><Keywords>*methods</Keywords><Keywords>Sensitivity and Specificity</Keywords><Keywords>Utah !! *a</Keywords><Reprint>Not in File</Reprint><Start_Page>491</Start_Page><End_Page>497</End_Page><Periodical>J Am Med Inform Assoc</Periodical><Volume>18</Volume><Issue>4</Issue><User_Def_2>MEDLINE - Ovid 11/10/2011, EMBASE (OVID) 9/22/2011, MEDLINE - Ovid 9/13/2011</User_Def_2><User_Def_3>Given to Fang Sun on 9/27/2011 for EPC0019</User_Def_3><ISSN_ISBN>21672911</ISSN_ISBN><Availability>Sharepoint , EPC0019 , SRPMEPC19_091311 , SREMEPC19_092111 , EPC19_Final_12-9-11</Availability><Address>Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah 84112, USA. axtinoco@</Address><ZZ_JournalStdAbbrev><f name="System">J Am Med Inform Assoc</f></ZZ_JournalStdAbbrev><ZZ_WorkformID>1</ZZ_WorkformID></MDL></Cite></Refman>6Two methods for detecting inpatient adverse drug events (ADEs) and hospital-associated infections (HAIs) were studied: A computerized surveillance system (CSS) or manual chart review (MCR)For CCS, the HELP (Health Evaluation through Logical Processing) system was used. This electronic system manages billing and administrative codes for each hospital admission, as well as information from several clinical domains: admission, discharge, and transfer (ADT)/registration, pharmacy, laboratory, microbiology, nurse charting, and physician narratives, etc. The physician narratives stored in the HELP system as freetext documents include emergency department report, admission history and physical report, consultant note, radiology report, surgical procedure note, and discharge summary.The HAI detection criteria used by CSS were originally based on the guidelines from the Study of the Efficacy of Nosocomial Infection Control and the Centers for Disease Control and Prevention (CDC). In addition to routine HAI surveillance, daily urine samples from all catheterized patients were obtained as part of an existing, hospital-wide urinary catheter surveillance program. The ADE detection criteria used by CSS include various clinical triggers such as medication discontinuation orders, dose decrease orders, antidote orders, laboratory test orders, abnormal laboratory test results and vital signs. Suspected cases are flagged by CSS and reported to surveillance personnel for validation. An infection preventionist or a clinical pharmacist verifies each HAI or ADE, respectively, using information from the patient record, direct bedside observations, and interviews with patients and their providers.The MCR data were from a previous multi-institutional research investigation of AEs (‘workload study’). No other detail was provided about how MCR was performed.The study retrospectively analyzed inpatient ADEs and HAIs detected either by CSS or MCR.Data were collected from 2,137?unique, prescreened admissions to the medical and surgical services of the LDS Hospital between October 1, 2000 and December 31, 2001.Descriptive analysis was performed for events detected using the two methods by type of AE, type of information about the AE, and sources of the information.External: None mentionedOrganizational Characteristics: The study was performed at LDS Hospital, a major teaching hospital in Salt Lake City, Utah.Teamwork: None mentionedLeadership: None mentionedCulture: None mentionedImplementation tools: For CSS, the HELP system was used, which has an integrated CSS that prospectively screens electronic patient data for indicators of AEs, including HAIs and ADEs. Refer to “Description of PSP” for more description about the HELP system.CSS detected more HAIs than MCR (92% vs. 34%); however, a?similar number of ADEs was detected by both systems (52%?vs. 51%). The agreement between systems was greater for HAIs than ADEs (26% vs. 3%). The CSS missed events that did?not have information in coded format or that were described only in physician narratives. The MCR detected events missed by CSS using information in physician narratives. Some ADEs found by MCR were detected by CSS but not verified by a clinician.References1. Olsen S, Neale G, Schwab K, et al. Hospital?staff should use more than one method to detect adverse events and potential adverse events: incident reporting, pharmacist surveillance and local real-time record review may all have a place. Qual Saf Health Care 2007 Feb;16(1):40-4. Also available: . PMID: 173012032. Wetzels R, Wolters R, van Weel C, et al. Mix?of methods is needed to identify adverse events in general practice: a prospective observational study. BMC Fam Pract 2008;9:35. PMID: 185544183. Ferranti J, Horvath MM, Cozart H, et al. A?multifaceted approach to safety: the synergistic detection of adverse drug events in adult inpatients. J Patient Saf 2008;4:184-90. Also available: . 4. Levinson DR. Adverse events in hospitals: methods for indentifying events [OEI-06-08-00221]. Washington (DC): Department of Health and Human Services, Office of Inspector General; 2010 Mar. 60 p. 5. Van Der Linden W, Warg A, Nordin P. National register study of operating time and outcome in hernia repair. Arch Surg 2011 Oct;146(10):1198-203. PMID: 220068806. Tinoco A, Evans RS, Staes CJ, et al. Comparison of computerized surveillance and manual chart review for adverse events. J Am Med Inform Assoc 2011 Jul-Aug;18(4):491-7. PMID: 21672911 ................
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