scholarly journals Should diagnosis codes from emergency department data be used for case selection for emergency department key performance indicators?

2014 ◽  
Vol 38 (1) ◽  
pp. 38 ◽  
Author(s):  
Stuart C. Howell ◽  
Rachael A. Wills ◽  
Trisha C. Johnston

Objective The aim of the present study was to assess the suitability of emergency department (ED) discharge diagnosis for identifying patient cohorts included in the definitions of key performance indicators (KPIs) that are used to evaluate ED performance. Methods Hospital inpatient episodes of care with a principal diagnosis that corresponded to an ED-defined KPI were extracted from the Queensland Hospital Admitted Patient Data Collection (QHAPDC) for the year 2010–2011. The data were then linked to the corresponding ED patient record and the diagnoses applied in the two settings were compared. Results The asthma and injury cohorts produced favourable results with respect to matching the QHAPDC principal diagnosis with the ED discharge diagnosis. The results were generally modest when the QHAPDC principal diagnosis was upper respiratory tract infection, poisoning and toxic effects or a mental health diagnosis, and were quite poor for influenza. Conclusions There is substantial variation in the capture of patient cohorts using discharge diagnosis as recorded on Queensland Hospital Emergency Department data. What is known about the topic? There are several existing KPIs that are defined according to the diagnosis recorded on ED data collections. However, there have been concerns over the quality of ED diagnosis in Queensland and other jurisdictions, and the value of these data in identifying patient cohorts for the purpose of assessing ED performance remains uncertain. What does this paper add? This paper identifies diagnosis codes that are suitable for use in capturing the patient cohorts that are used to evaluate ED performance, as well as those codes that may be of limited value. What are the implications for practitioners? The limitations of diagnosis codes within ED data should be understood by those seeking to use these data items for healthcare planning and management or for research into healthcare quality and outcomes.

2019 ◽  
Vol 2 ◽  
pp. 18 ◽  
Author(s):  
Aileen McCabe ◽  
Maria Brenner ◽  
Philip Larkin ◽  
Sinéad Nic An Fhailí ◽  
Brenda Gannon ◽  
...  

Background: Good-quality data is required for valid and reliable key performance indicators. Little is known of the facilitators and barriers of capturing the required data for emergency department key performance indicators. This study aimed to explore and understand how current emergency department data collection systems relevant to emergency department key performance indicators are integrated into routine service delivery, and to identify the resources required to capture these data elements. Methods: Following pilot testing, we conducted two focus groups with a multi-disciplinary panel of 14 emergency department stakeholders drawn from urban and rural emergency departments, respectively. Focus groups were analyzed using Attride–Stirling’s framework for thematic network analysis. Results: The global theme “Understanding facilitators and barriers for emergency department data collection systems” emerged from three organizing themes: “understanding current emergency department data collection systems”; “achieving the ideal emergency department data capture system for the implementation of emergency department key performance indicators”; and “emergency department data capture systems for performance monitoring purposes within the wider context”. Conclusion: The pathways to improving emergency department data capture systems for emergency department key performance indicators include upgrading emergency department information systems and investment in hardware technology and data managers. Educating stakeholders outside the emergency department regarding the importance of emergency department key performance indicators as hospital-wide performance indicators underpins the successful implementation of valid and reliable emergency department key performance indicators.


2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Emery Shekiro ◽  
Lily Sussman ◽  
Talia Brown

Objective: In order to better describe local drug-related overdoses, we developed a novel syndromic case definition using discharge diagnosis codes from emergency department data in the Colorado North Central Region (CO-NCR). Secondarily, we used free text fields to understand the use of unspecified diagnosis fields.Introduction: The United States is in the midst of a drug crisis; drug-related overdoses are the leading cause of unintentional death in the country. In Colorado the rate of fatal drug overdose increased 68% from 2002-2014 (9.7 deaths per 100,000 to 16.3 per 100,000, respectively)1, and non-fatal overdose also increased during this time period (23% increase in emergency department visits since 2011)2. The CDC’s National Syndromic Surveillance Program (NSSP) provides near-real time monitoring of emergency department (ED) events across the country, with information uploaded daily on patient demographics, chief complaint for visit, diagnosis codes, triage notes, and more. Colorado North Central Region (CO-NCR) receives data for 4 local public health agencies from 25 hospitals across Adams, Arapahoe, Boulder, Denver, Douglas, and Jefferson Counties.Access to local syndromic data in near-real time provides valuable information for local public health program planning, policy, and evaluation efforts. However, use of these data also comes with many challenges. For example, we learned from key informant interviews with ED staff in Boulder and Denver counties, about concern with the accuracy and specificity of drug-related diagnosis codes, specifically for opioid-related overdoses.Methods: Boulder County Public Health (BCPH) and Denver Public Health (DPH) developed a query in Early Notification of Community Based Epidemics (ESSENCE) using ICD-10-CM codes to identify cases of drug-related overdose [T36-T51] from October 2016 to September 2017. The Case definition included unintentional, self-harm, assault and undetermined poisonings, but did not include cases coded as adverse effects or underdosing of medication. Cases identified in the query were stratified by demographic factors (i.e., gender and age) and substance used in poisoning. The first diagnosis code in the file was considered the primary diagnosis. Chief complaint and triage note fields were examined to further describe unspecified cases and to describe how patients present to emergency departments in the CO-NCR. We also explored whether detection of drug overdose visits captured by discharge diagnosis data varied by patient sex, age, or county.Results: The query identified 2,366 drug-related overdoses in the CO-NCR. The prevalence of drug overdoses differed across age groups. The detection of drug overdoses was highest among our youth and young adult populations; 16 to 20 year olds (16.0%), 21-25 year olds (11.4%), 26-30 year olds (11.4%). Females comprised 56.1% of probable general drug overdoses. The majority of primary diagnoses (31.0%) included poisonings related to diuretics and other unspecified drugs (T50), narcotics (T40) (12.6%), or non-opioid analgesics (T39) (7.8%). For some cases with unspecified drug overdose codes there was additional information about drugs used and narcan administration found in the triage notes and chief complaint fields.Conclusions: Syndromic surveillance offers the opportunity to capture drug-related overdose data in near-real time. We found variation in drug-related overdose by demographic groups. Unspecified drug overdose codes are extremely common, which likely negatively impacts the quality of drug-specific surveillance. Leveraging chief complaint and triage notes could improve our understanding of factors involved in drug-related overdose with limitations in discharge diagnosis. Chart reviews and access to more fields from the ED electronic health record could improve local drug surveillance.


2017 ◽  
Vol 24 (3) ◽  
pp. 196-201 ◽  
Author(s):  
Brenda Gannon ◽  
Cheryl Jones ◽  
Aileen McCabe ◽  
Ronan O’Sullivan ◽  
Abel Wakai

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Lily Sussman ◽  
Talia Brown

ObjectiveIn order to meet local mental health surveillance needs, we created multiple mental health-related indicators using emergency department data from the Colorado North Central Region (CO-NCR) Early Notification of Community Based Epidemics (ESSENCE), a Syndromic Surveillance (SyS) platform.IntroductionMental health is a common and costly concern; it is estimated that nearly 20 percent of adults in the United States live with a mental illness[1] and that more money is spent on mental illness than any other medical condition.[2] One spillover effect of unmet mental health needs may be increasing emergency department utilization. National analysis by Healthcare Cost and Utilization Project (H-CUP) found a 55% increase in emergency department visits for depression, anxiety, and stress reactions between 2006-2013.[3] Local public health agencies (LPHAs) can play an important role in reducing costs and burden associated with mental illness. There is opportunity to use emergency department data at a local level to monitor trends and evaluate the effectiveness of local strategies. ESSENCE, available in 31 states, provides near-real time observation-level emergency department data, which can be analyzed and disseminated according to local needs. Using ESSENCE data from 6 local counties in Colorado, we developed methods to estimate the overall burden of mental health and specific mental health disorders seen in the emergency department.MethodsBoulder County Public Health expanded on existing methods to develop multiple mental health queries in ESSENCE using data from the six Colorado counties that currently participate in the Colorado North Central Region (CO-NCR) SyS (i.e., Adams, Arapahoe, Boulder, Denver, Douglas, and Jefferson Counties). Our query was based solely off relevant International Classification of Disease version 10 Clinical Modification (ICD-10-CM) mental health codes: F20-F48, F99, R45.851, X71–X83, T14.91, and R45.851. We also included T36-T65 and T71 where intentional self-harm was specified. In addition to an overall mental health query we created 11 sub-queries for: anxiety disorder, conversion disorder, intentional self-harm/suicide attempt, mood disorder, obsessive compulsive disorder (OCD), dissociative disorder, schizophrenia, somatoform disorders, stress adjustment disorder, suicide ideation, and other mental health disorder). One observation could fall into multiple subcategories through inclusion of multiple discharge diagnosis (DD).One challenge of using the DD field in ESSENCE is that in Colorado, similar to other states, there can be excess of 40 unique ICD-10-CM codes listed in the DD field, and queries identify cases by searching all listed codes. For this project, that is problematic as codes may refer to historic and underlying health conditions, rather than acute cause of the ED visit. To handle this, we performed a secondary analysis to determine whether observations were “true mental health cases” based on order of codes listed in DD field, triage notes and chief complaint. We then calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value(NPV) of including observations where mental health was listed as the first (or primary) code, first or second, or first second or third code. Our analysis revealed that observations where mental health codes are listed later were less likely to be identifiable as true mental health cases, and led to our decision to only include observations with qualifying codes listed first or second.To assess the mental health burden, we developed code in SAS 9.4 that parsed ESSENCE output by discharge diagnosis, create aforementioned sub-queries, and calculated counts and age-adjusted rates (based on 2000 US Population) to summarize demographic and geographic trends.ResultsThere were 22,451 observations with mental health discharge diagnosis codes for the six Colorado counties between January and June 2018. Of these codes, 13,331 had a mental health code as the first and/or second listed DD and were counted as true mental health visits. The age-adjusted rates of any mental health visit ranged from approximately 425 per 100,000 in Douglas County to 1,026 per 100,000 in Denver County. The most common reasons for mental health visits across the region were anxiety, mood disorder, and suicide ideation (Figure 1). There was a significant spike in mental health ED visits among the 15-24 age group, followed by decreasing rates in older age groups (Figure 2). Younger age groups most commonly had ED visits for mood disorder (all age groups under 24), while in the age groups 25-34, 35-44, 65-74 and 75+ the most common reason for ED visit was anxiety. Also of note, ED visits for suicide ideation and self- harm were highest for the 15-24 age group. Males and females had similar rates of ED visits for most diagnoses, which is notable given males generally utilize healthcare services at lower rates than females.ConclusionsSyndromic surveillance is a valuable addition to available mental health surveillance. Our methods and results demonstrate the feasibility of tracking overall and specific mental health trends using the ESSENCE platform. Unlike other available mental health data, ESSENCE provides data that is local, observation level, and near-real time. Through continued collaboration with public health, medical and other stakeholders we hope this data can be pivotal in gauging disparities in mental health burden, monitoring trends, and prioritizing solutions.References[1] Mental Illness. National Institute of Mental Health. https://www.nimh.nih.gov/health/statistics/mental-illness.shtml[2] Roehrig C. Mental Disorders Top The List Of The Most Costly Conditions In The United States: $201 Billion. Health Aff (Millwood). 2016 Jun 1;35(6):1130-5. https://www-healthaffairs-org.ezp.welch.jhmi.edu/doi/pdf/10.1377/hlthaff.2015.1659[3]Weiss AJ, Barrett ML, Heslin KC. , Stocks C. Trends in Emergency Department Visits Involving Mental and Substance Use Disorders, 2006-2013. HCUP Statistical Brief #216. Agency for Healthcare Research and Quality. http://www.hcup-us.ahrq.gov/reports/statbriefs/sb216-Mental-Substance-Use-Disorder-ED-Visit-Trends.pdf. December 2016.


2008 ◽  
Vol 32 (2) ◽  
pp. 246 ◽  
Author(s):  
Biswadev Mitra ◽  
Peter A Cameron ◽  
Greg Mele ◽  
Peter Archer

The aim of this study was to pilot a program to encourage shift breaks for emergency department doctors and analyse the effects of breaks on tiredness and fatigue as well as possible effects on overall departmental performance. During Phase 1, medical staff were asked to fill out a survey regarding their working day at the end of every shift. A 30-minute uninterrupted break was promoted during Phase 2 by provision of a cover doctor on the roster as well as educational sessions and posters. There were 233 completed surveys received over the 4-week period. Only 33% of shifts worked included an uninterrupted break in Phase 1, which improved significantly to 60% during Phase 2. Subjective tiredness was significantly lower at the end of a shift when a break was taken (P < 0.001), while fatigue levels were also lower, but not significant (P = 0.060). There were significant improvements in some key performance indicators.


CCIT Journal ◽  
2012 ◽  
Vol 6 (1) ◽  
pp. 17-34
Author(s):  
Untung Rahardja ◽  
Muhamad Yusup Eva ◽  
Rosyifa Rosyifa

SQL Server Reporting Services is a way to analyze data, create reports using the indicators and gauges. Indicators are minimal gauges that convey the state of a single data value at a glance, and most are used to represent the state of Key Performance Indicators. Manage and harmonize the performance of an institution's educational institutions, especially universities with the performance of individuals or resources, no doubt is one of the essential elements for the success of an entity of the institution. Integrate the performance of an educational institution with individual performance is not an easy process, and therefore required a systematic approach to manage it. Implementation of a strategic management system based Balanced Scorecard can be used as a performance measurement system that will continuously monitor the successful implementation of the strategy of any public educational institution and measure the performance of its resources in a comprehensive and balanced, not the quantity but the emphasis is more concerned with the quality, so the performance of educational institutions at any time can be known clearly. Contribution of Key Performance Indicators to manage and harmonize the performance of any public institution is a solution in providing information to realize the extent of work that has set targets, identify and monitor measures of success, of course, with performance indicators show a clear, specific and measurable.


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