Literature DB >> 22435964

The promise and peril of healthcare forecasting.

J Frank Wharam1, Jonathan P Weiner.   

Abstract

Health plans and physician groups increasingly use sophisticated tools to predict individual patient outcomes. Such analytics will accelerate as US medicine enters the digital age. Promising applications of forecasting include better targeting of disease management as well as innovative patient care approaches such as personalized health insurance and clinical decision support systems. In addition, stakeholders will use predictions to advance their organizational agendas, and unintended consequences could arise. Forecasting-based interventions might have uncertain effectiveness, focus on cost savings rather than long-term health, or specifically exclude disadvantaged populations. Policy makers, health plans, and method developers should adopt strategies that address these concerns in order to maximize the benefit of healthcare forecasting on the long-term health of patients.

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Year:  2012        PMID: 22435964

Source DB:  PubMed          Journal:  Am J Manag Care        ISSN: 1088-0224            Impact factor:   2.229


  10 in total

1.  Total and out-of-pocket expenditures among women with metastatic breast cancer in low-deductible versus high-deductible health plans.

Authors:  Christine Leopold; Anita K Wagner; Fang Zhang; Christine Y Lu; Craig C Earle; Larissa Nekhlyudov; Dennis Ross-Degnan; J Frank Wharam
Journal:  Breast Cancer Res Treat       Date:  2018-06-01       Impact factor: 4.872

Review 2.  A review of analytics and clinical informatics in health care.

Authors:  Allan F Simpao; Luis M Ahumada; Jorge A Gálvez; Mohamed A Rehman
Journal:  J Med Syst       Date:  2014-04-03       Impact factor: 4.460

3.  The Analytic Information Warehouse (AIW): a platform for analytics using electronic health record data.

Authors:  Andrew R Post; Tahsin Kurc; Sharath Cholleti; Jingjing Gao; Xia Lin; William Bornstein; Dedra Cantrell; David Levine; Sam Hohmann; Joel H Saltz
Journal:  J Biomed Inform       Date:  2013-02-09       Impact factor: 6.317

4.  Predictive modeling using a nationally representative database to identify patients at risk of developing microalbuminuria.

Authors:  Lorenzo Villa-Zapata; Terri Warholak; Marion Slack; Daniel Malone; Anita Murcko; George Runger; Michael Levengood
Journal:  Int Urol Nephrol       Date:  2015-12-11       Impact factor: 2.370

5.  Proposals for enhanced health risk assessment and stratification in an integrated care scenario.

Authors:  Ivan Dueñas-Espín; Emili Vela; Steffen Pauws; Cristina Bescos; Isaac Cano; Montserrat Cleries; Joan Carles Contel; Esteban de Manuel Keenoy; Judith Garcia-Aymerich; David Gomez-Cabrero; Rachelle Kaye; Maarten M H Lahr; Magí Lluch-Ariet; Montserrat Moharra; David Monterde; Joana Mora; Marco Nalin; Andrea Pavlickova; Jordi Piera; Sara Ponce; Sebastià Santaeugenia; Helen Schonenberg; Stefan Störk; Jesper Tegner; Filip Velickovski; Christoph Westerteicher; Josep Roca
Journal:  BMJ Open       Date:  2016-04-15       Impact factor: 2.692

6.  Impact of emerging health insurance arrangements on diabetes outcomes and disparities: rationale and study design.

Authors:  J Frank Wharam; Steve Soumerai; Connie Trinacty; Emma Eggleston; Fang Zhang; Robert LeCates; Claire Canning; Dennis Ross-Degnan
Journal:  Prev Chronic Dis       Date:  2013       Impact factor: 2.830

7.  Comparison of physician referral and insurance claims data-based risk prediction as approaches to identify patients for care management in primary care: an observational study.

Authors:  Tobias Freund; Matthias Gondan; Justine Rochon; Frank Peters-Klimm; Stephen Campbell; Michel Wensing; Joachim Szecsenyi
Journal:  BMC Fam Pract       Date:  2013-10-20       Impact factor: 2.497

8.  Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework.

Authors:  Hamzeh Khazaei; Carolyn McGregor; J Mikael Eklund; Khalil El-Khatib
Journal:  JMIR Med Inform       Date:  2015-11-18

Review 9.  Big data for bipolar disorder.

Authors:  Scott Monteith; Tasha Glenn; John Geddes; Peter C Whybrow; Michael Bauer
Journal:  Int J Bipolar Disord       Date:  2016-04-11

10.  Effect of High-Deductible Insurance on High-Acuity Outcomes in Diabetes: A Natural Experiment for Translation in Diabetes (NEXT-D) Study.

Authors:  J Frank Wharam; Fang Zhang; Emma M Eggleston; Christine Y Lu; Stephen B Soumerai; Dennis Ross-Degnan
Journal:  Diabetes Care       Date:  2018-01-30       Impact factor: 17.152

  10 in total

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