Cost-Effective Enhancement of Claims Data to Improve Comparisons of Patient Safety

Jordan, H; Pine, Michael; Elixhauser, Anne; Hoaglin, David C.; Fry, Donald E.; Coleman, Kevin; Deitz, Deborah; Warner, David; Gonzales, Junius; Friedman, Zachary

doi:10.1097/01.jps.0000242988.01413.fb

Cited by 15 publications

(6 citation statements)

References 18 publications

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“…Recent publications demonstrated that automated laboratory data offer clinical credibility, objectivity, parsimony, and cost‐effectiveness for risk adjustment (Jordan et al 2007; Tabak, Johannes, and Silber 2007; Escobar et al 2008; Render et al 2008). Laboratory data were found to contribute most in predicting mortality among demographics, comorbidities, and other groups of variables (Tabak, Johannes, and Silber 2007; Escobar et al 2008; Render et al 2008).…”

mentioning

confidence: 99%

Development and Validation of a Disease‐Specific Risk Adjustment System Using Automated Clinical Data

Tabak¹,

Sun²,

Derby³

et al. 2010

Health Services Research

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Objective. To develop and validate a disease-specific automated inpatient mortality risk adjustment system primarily using computerized numerical laboratory data and supplementing them with administrative data. To assess the values of additional manually abstracted data. Methods. Using 1,271,663 discharges in 2000-2001, we derived 39 disease-specific automated clinical models with demographics, laboratory findings on admission, ICD-9 principal diagnosis subgroups, and secondary diagnosis-based chronic conditions. We then added manually abstracted clinical data to the automated clinical models (manual clinical models). We compared model discrimination, calibration, and relative contribution of each group of variables. We validated these 39 models using 1,178,561 discharges in 2004-2005. Results. The overall mortality was 4.6 percent (n 5 58,300) and 4.0 percent (n 5 47,279) for derivation and validation cohorts, respectively. Common mortality predictors included age, albumin, blood urea nitrogen or creatinine, arterial pH, white blood counts, glucose, sodium, hemoglobin, and metastatic cancer. The average c-statistic for the automated clinical models was 0.83. Adding manually abstracted variables increased the average c-statistic to 0.85 with better calibration. Laboratory results displayed the highest relative contribution in predicting mortality. Conclusions. A small number of numerical laboratory results and administrative data provided excellent risk adjustment for inpatient mortality for a wide range of clinical conditions.

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mentioning

confidence: 99%

Development and Validation of a Disease‐Specific Risk Adjustment System Using Automated Clinical Data

Tabak¹,

Sun²,

Derby³

et al. 2010

Health Services Research

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“…Similarly, previous studies have shown improvements in predictions of inpatient mortality and complications as a result of linking claims data to hospital numerical laboratory data (Jordan et al. ; Pine et al. ).…”

mentioning

confidence: 69%

Enhancing Clinical Content and Race/Ethnicity Data in Statewide Hospital Administrative Databases: Obstacles Encountered, Strategies Adopted, and Lessons Learned

Pine

Kowlessar

Salemi

et al. 2015

Health Services Research

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Objectives. Eight grant teams used Agency for Healthcare Research and Quality infrastructure development research grants to enhance the clinical content of and improve race/ethnicity identifiers in statewide all-payer hospital administrative databases. Principal Findings. Grantees faced common challenges, including recruiting data partners and ensuring their continued effective participation, acquiring and validating the accuracy and utility of new data elements, and linking data from multiple sources to create internally consistent enhanced administrative databases. Successful strategies to overcome these challenges included aggressively engaging with providers of critical sources of data, emphasizing potential benefits to participants, revising requirements to lessen burdens associated with participation, maintaining continuous communication with participants, being flexible when responding to participants' difficulties in meeting program requirements, and paying scrupulous attention to preparing data specifications and creating and implementing protocols for data auditing, validation, cleaning, editing, and linking. In addition to common challenges, grantees also had to contend with unique challenges from local environmental factors that shaped the strategies they adopted. Conclusions. The creation of enhanced administrative databases to support comparative effectiveness research is difficult, particularly in the face of numerous challenges with recruiting data partners such as competing demands on information technology resources. Excellent communication, flexibility, and attention to detail are essential ingredients in accomplishing this task. Additional research is needed to develop strategies for maintaining these databases when initial funding is exhausted.

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“…94,121,143 Evidence remains inconclusive regarding the ability of administrative data algorithms to consistently and accurately detect adverse events that occur in hospitals. 39,44,55,83,144 The lack of POA flags is a notable problem for many databases. Large validation studies of indicators, such as the current multicenter evaluation by AHRQ, are necessary to develop the usability of measures, enhance their sensitivity and specificity, understand better the effects of bias and risk adjustment, and consider how differences in health care systems may affect the use of indicators.…”

Section: Coding Barriers To Wider Usementioning

confidence: 99%

“…24,39,47,118,122 Similarly, the validity and clinical value of applying administrative data-based patient safety measures in patient groups beyond the narrowly defined populations at risk of some validation exercises require additional investigation. 55 Because of the nature of ICD-9-CM coding, currently available indicators are more accurate in detecting surgery-related adverse events. 77,98 This may help explain why we found more studies on surgical adverse events than studies relating to patient harm in other specialties.…”

Section: Implications For Research and Practicementioning

confidence: 99%

A Review of Patient Safety Measures Based on Routinely Collected Hospital Data

et al. 2011

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The literature on patient safety measures derived from routinely collected hospital data was reviewed to inform indicator development. MEDLINE and Embase databases and Web sites were searched. Of 1738 citations, 124 studies describing the application, evaluation, or validation of hospital-based medical error or complication of care measures were reviewed. Studies were frequently conducted in the United States (n = 88) between 2005 and 2009 (n = 77) using Agency for Healthcare Research and Quality patient safety indicators (PSIs; n = 79). The most frequently cited indicators included "postoperative hemorrhage or hematoma" and "accidental puncture and laceration." Indicator refinement is supported by international coding algorithm translations but is hampered by data issues, including coding inconsistencies. The validity of PSIs and similar adverse event screens beyond internal measurement and the effects of organizational factors on patient harm remain uncertain. Development of PSIs in ambulatory care settings, including general practice and psychiatric care, needs consideration.

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Cost-Effective Enhancement of Claims Data to Improve Comparisons of Patient Safety

Cited by 15 publications

References 18 publications

Development and Validation of a Disease‐Specific Risk Adjustment System Using Automated Clinical Data

Development and Validation of a Disease‐Specific Risk Adjustment System Using Automated Clinical Data

Enhancing Clinical Content and Race/Ethnicity Data in Statewide Hospital Administrative Databases: Obstacles Encountered, Strategies Adopted, and Lessons Learned

A Review of Patient Safety Measures Based on Routinely Collected Hospital Data

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