Biomedical subjects

Iver A Juster

Publications and source records attributed to Iver A Juster.

3 recordsLinked to original sources

PubMed2005

Technology-driven interactive care management identifies and resolves more clinical issues than a claims-based alerting system.

Due to patient or physician factors, people with chronic diseases frequently do not receive evidence-based care. While a physician-directed claims-based alerting system targeting gaps in care was previously shown to increase resolution of specific clinical issues, many apparently relevant issues remained unresolved. The purpose of this research was to demonstrate that adding member interaction with a nurse to a physician alerting system can uncover additional care gaps beyond those identified by a claims, prescription, and lab results-based alerting system, and increase successful resolution of alerts by communicating care gaps to members. An opt-in nurse-managed pilot program focusing on identification and resolution of specific clinical issues was implemented for 205,463 members of self-insured health plans that had been utilizing the claims-based physician alerting system. Specific clinical issues identified by the claims-based system were communicated to both program enrollees and physicians, and new clinical issues were identified based on nurse-directed participant feedback. Participants were encouraged to discuss issues with their physicians. Issue resolution rates were tracked using subsequent claims, pharmacy, and lab data. At 1 year, we studied the rate of new clinical issue identification and compared the program's resolution rate of claims-identifiable issues to that of non-enrollees. While program participants accounted for 0.65% of total member-months in the pilot year, they triggered 4.82% (644) of the population's claims-based clinical alerts, and an additional 514 alerts from data based on participant-supplied data--80.8% more than claims/pharmacy/lab-generated alerts. Of the participants' claims-based alerts, 207 (32.1%) showed claims/lab evidence of successful resolution, compared with 3,380 of 12,714 (26.6%) for non-participants, a 20.9% increase in resolutions (chi2 = 9.8, p < 0.01). Care management technology complemented by a nurse-directed interactive program increased the rate of identification of clinical issues compared to claims alerts alone. Use of this program to communicate specific issues to both patients and physicians significantly increased the rate of issue resolution.

Decision Support Systems, Clinical↗

PubMed2005

Practical Clues to Early Recognition of Bipolar Disorder: A Primary Care Approach.

Early treatment can favorably impact the course of bipolar disorder, a lifelong illness. Because bipolar disorder can masquerade as various mental and physical illnesses-primarily major depressive disorder-patients with this condition frequently go unrecognized for years. During this recognition lag, such patients may present to their primary care physician on multiple occasions. Accordingly, primary care physicians would benefit from knowing the "clues" to early recognition of the disorder, because early recognition and management can reduce disability, improve social and employment stability, and result in improved functional outcomes. This review describes 3 pathways to the diagnosis of bipolar disorder relevant to the primary care setting: detection of mania or hypomania, differential diagnosis of recurrent depressive episodes, and identification of interepisode disorder and its comorbidities. We summarize these pathways in terms of a practical tool that a primary care physician can use to trigger further evaluation or referral.

Journal Article↗

PubMed2005

Use of administrative data to identify health plan members with unrecognized bipolar disorder: a retrospective cohort study.

OBJECTIVE: This retrospective cohort study used an algorithmic case-finding system on claims data from nationwide commercial health plans to validate previously identified predictors of unrecognized bipolar disorder among adults. STUDY DESIGN: Retrospective cohort design. METHODS: Using logistic regression, 2 claims data sets were evaluated to explore potential predictors; the first included claims for all healthcare encounters (all-encounters data set); the second excluded mental health provider claims (carve-out data set). A total of 280,244 members aged 18 to 64 years were included from 2 commercial health plans. RESULTS: Claims related to attention deficit-hyperactivity disorder, depression, depression treated with antipsychotics, use of 3 (of 5) classes of psychotherapeutic drugs, younger age, and sex were all significant predictors of a subsequent diagnosis of bipolar disorder. In the all-encounters data set, a predicted value of 5% or greater yielded a sensitivity of 9.8% and a specificity of 99.9%; a predicted threshold of 3% increased sensitivity to 20.7%; area under the receiver operating characteristic curve (AUC) was 0.82. Performance of the model was acceptable in the carve-out data set, with AUC 0.69. CONCLUSIONS: The case-finding system described here, which compares favorably with other screening tests used in primary care, may have significant value in helping physicians to identify patients with unrecognized bipolar disorder.

Adolescent↗