How Often are Potential Patient Safety Events Present on Admission?

Houchens, Robert; Elixhauser, Anne; Romano, Patrick S.

doi:10.1016/s1553-7250(08)34018-5

Cited by 104 publications

(108 citation statements)

References 14 publications

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“…Indeed, there were some diagnoses coded as postadmit and thus identified in step 1 of our study that clinical experts recognized as being improperly coded. Limited published findings from both Canada 31,32 and the US [33][34][35] suggest that the accuracy of diagnosis timing information is not perfect, and that it may vary across jurisdictions, hospitals, and conditions. Overreporting of conditions as being POA may cause underdetection of adverse events, whereas other conditions that are accurately coded as postadmit may have resulted from the trajectory of illness, which started before admission and was unresponsive to appropriate treatment (eg, delirium or renal failure in a patient admitted with sepsis).…”

Section: Discussionmentioning

confidence: 99%

Deriving ICD-10 Codes for Patient Safety Indicators for Large-scale Surveillance Using Administrative Hospital Data

et al. 2017

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Background: Existing administrative data patient safety indicators (PSIs) have been limited by uncertainty around the timing of onset of included diagnoses. Objective: We undertook de novo PSI development through a data-driven approach that drew upon “diagnosis timing” information available in some countries’ administrative hospital data. Research Design: Administrative database analysis and modified Delphi rating process. Subjects: All hospitalized adults in Canada in 2009. Measures: We queried all hospitalizations for ICD-10-CA diagnosis codes arising during hospital stay. We then undertook a modified Delphi panel process to rate the extent to which each of the identified diagnoses has a potential link to suboptimal quality of care. We grouped the identified quality/safety-related diagnoses into relevant clinical categories. Lastly, we queried Alberta hospital discharge data to assess the frequency of the newly defined PSI events. Results: Among 2,416,413 national hospitalizations, we found 2590 unique ICD-10-CA codes flagged as having arisen after admission. Seven panelists evaluated these in a 2-round review process, and identified a listing of 640 ICD-10-CA diagnosis codes judged to be linked to suboptimal quality of care and thus appropriate for inclusion in PSIs. These were then grouped by patient safety experts into 18 clinically relevant PSI categories. We then analyzed data on 2,381,652 Alberta hospital discharges from 2005 through 2012, and found that 134,299 (5.2%) hospitalizations had at least 1 PSI diagnosis. Conclusion: The resulting work creates a foundation for a new set of PSIs for routine large-scale surveillance of hospital and health system performance.

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Section: Discussionmentioning

confidence: 99%

Deriving ICD-10 Codes for Patient Safety Indicators for Large-scale Surveillance Using Administrative Hospital Data

et al. 2017

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“…Measures of undesirable events include the following risk-adjusted PSIs that Savitz et al (2005) indicate are sensitive to nurse staffing: failure to rescue, infection due to medical care, postoperative respiratory failure, and postoperative sepsis. Decubitus ulcer and postoperative pulmonary embolism (PE) or deep vein thrombosis (DVT) are also nurse-sensitive measures of quality; however, Houchens et al (2008) found that a high percentage of these events are present on admission (POA). Therefore, they are not valid measures of hospital quality, except in data systems that include POA information., and we exclude them from our study.…”

Section: Datamentioning

confidence: 99%

High versus lower quality hospitals: a comparison of environmental characteristics and technical efficiency

Mutter¹,

Valdmanis

Rosko

2010

Health Serv Outcomes Res Method

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We seek to determine whether hospitals providing high-quality care are associated with different environmental and organizational factors than hospitals providing lower quality of care. We address this question using congestion analysis, which is an extension of data envelopment analysis (DEA). We employ a rich data set comprised of urban hospitals operating in 34 states in 2004. The data include measures of inputs, outputs, case mix, and indicators of patient safety, which we use to derive separate bestpractice frontiers for high-, medium-, and low-quality hospitals. By comparing the frontier of the high-quality hospitals with the lower quality hospital frontiers, we find that higher quality hospitals perform better on the criteria of overall technical efficiency and pure technical efficiency than lower quality institutions. We found that a variety of characteristics, including expenditures, percentage of admissions that are births, Medicaid share of admissions, and government ownership are associated with differences in performance among the medium-quality hospitals. Expenditures and birth rate were associated with differences within low-quality hospital performance, as were variables related to scope and depth of activities, such as capital expenditures per bed and teaching status. We find evidence that hospitals should be able to improve both the quality and efficiency of their operations by adopting, where possible, the characteristics of the highest performing institutions.R. Mutter (

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“…Concerns exist about the validity of administrative codes, [7][8][9] and it can be difficult to determine from discharge diagnostic codes whether a disease entity existed before the patient was hospitalized or occurred during the hospital admission. 10,11 With the rapid expansion of electronic medical record (EMR) use, along with increased federal support for health care information technology, a far richer source of clinical information regarding hospital-related safety events has emerged. 12 The development of automated approaches, such as natural language processing, that extract specific medical concepts from textual medical documents that do not rely on discharge codes offers a powerful alternative to either unreliable administrative data or labor-intensive, expensive manual chart reviews.…”

mentioning

confidence: 99%

Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing

Murff

FitzHenry²,

Matheny³

et al. 2011

JAMA

384

285

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How Often are Potential Patient Safety Events Present on Admission?

Cited by 104 publications

References 14 publications

Deriving ICD-10 Codes for Patient Safety Indicators for Large-scale Surveillance Using Administrative Hospital Data

Deriving ICD-10 Codes for Patient Safety Indicators for Large-scale Surveillance Using Administrative Hospital Data

High versus lower quality hospitals: a comparison of environmental characteristics and technical efficiency

Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing

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