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The interaction of direct and indirect risk selection.

Journal of health economics

Lorenz N.
PMID: 25935738
J Health Econ. 2015 Jul;42:81-9. doi: 10.1016/j.jhealeco.2014.12.003. Epub 2014 Dec 17.

This paper analyzes the interaction of direct and indirect risk selection in health insurance markets. It is shown that direct risk selection - using measures unrelated to the benefit package like selective advertising or 'losing' applications of high risk...

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Lorenz N. The interaction of direct and indirect risk selection. J Health Econ. 2014;42:81-9doi: 10.1016/j.jhealeco.2014.12.003.
Lorenz, N. (2015). The interaction of direct and indirect risk selection. Journal of health economics, 4281-9. https://doi.org/10.1016/j.jhealeco.2014.12.003
Lorenz, Normann. "The interaction of direct and indirect risk selection." Journal of health economics vol. 42 (2015): 81-9. doi: https://doi.org/10.1016/j.jhealeco.2014.12.003
Lorenz N. The interaction of direct and indirect risk selection. J Health Econ. 2015 Jul;42:81-9. doi: 10.1016/j.jhealeco.2014.12.003. Epub 2014 Dec 17. PMID: 25935738.
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The clinical usefulness of prognostic prediction models in critical illness.

European journal of internal medicine

Baker T, Gerdin M.
PMID: 28935477
Eur J Intern Med. 2017 Nov;45:37-40. doi: 10.1016/j.ejim.2017.09.012. Epub 2017 Sep 19.

Critical illness is any immediately life-threatening disease or trauma and results in several million deaths globally every year. Responsive hospital systems for managing critical illness include quick and accurate identification of the critically ill patients. Prognostic prediction models are...

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Baker T, Gerdin M. The clinical usefulness of prognostic prediction models in critical illness. Eur J Intern Med. 2017;45:37-40doi: 10.1016/j.ejim.2017.09.012.
Baker, T., & Gerdin, M. (2017). The clinical usefulness of prognostic prediction models in critical illness. European journal of internal medicine, 4537-40. https://doi.org/10.1016/j.ejim.2017.09.012
Baker, Tim, and Gerdin, Martin. "The clinical usefulness of prognostic prediction models in critical illness." European journal of internal medicine vol. 45 (2017): 37-40. doi: https://doi.org/10.1016/j.ejim.2017.09.012
Baker T, Gerdin M. The clinical usefulness of prognostic prediction models in critical illness. Eur J Intern Med. 2017 Nov;45:37-40. doi: 10.1016/j.ejim.2017.09.012. Epub 2017 Sep 19. PMID: 28935477.
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Risk adjustment in analysis of surgery for congenital heart disease.

The Journal of thoracic and cardiovascular surgery

Shuhaiber JH.
PMID: 12771898
J Thorac Cardiovasc Surg. 2003 May;125(5):1171; author reply 1173. doi: 10.1067/mtc.2003.270.

No abstract available.

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Shuhaiber JH. Risk adjustment in analysis of surgery for congenital heart disease. J Thorac Cardiovasc Surg. 2003;125(5):1171; author reply 1173doi: 10.1067/mtc.2003.270.
Shuhaiber, J. H. (2003). Risk adjustment in analysis of surgery for congenital heart disease. The Journal of thoracic and cardiovascular surgery, 125(5), 1171; author reply 1173. https://doi.org/10.1067/mtc.2003.270
Shuhaiber, Jeffrey H. "Risk adjustment in analysis of surgery for congenital heart disease." The Journal of thoracic and cardiovascular surgery vol. 125,5 (2003): 1171; author reply 1173. doi: https://doi.org/10.1067/mtc.2003.270
Shuhaiber JH. Risk adjustment in analysis of surgery for congenital heart disease. J Thorac Cardiovasc Surg. 2003 May;125(5):1171; author reply 1173. doi: 10.1067/mtc.2003.270. PMID: 12771898.
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The underwriting cycle: the rule of six.

Health affairs (Project Hope)

Rosenblatt A.
PMID: 15537588
Health Aff (Millwood). 2004 Nov-Dec;23(6):103-6. doi: 10.1377/hlthaff.23.6.103.

The underwriting cycle is a thing of the past for most health insurance companies. There were six primary factors that caused the six-year pattern of the underwriting cycle for 1965-1991. These factors were claims payment cycle time, renewal dates...

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Rosenblatt A. The underwriting cycle: the rule of six. Health Aff (Millwood). 2004;23(6):103-6doi: 10.1377/hlthaff.23.6.103.
Rosenblatt, A. (2004). The underwriting cycle: the rule of six. Health affairs (Project Hope), 23(6), 103-6. https://doi.org/10.1377/hlthaff.23.6.103
Rosenblatt, Alice. "The underwriting cycle: the rule of six." Health affairs (Project Hope) vol. 23,6 (2004): 103-6. doi: https://doi.org/10.1377/hlthaff.23.6.103
Rosenblatt A. The underwriting cycle: the rule of six. Health Aff (Millwood). 2004 Nov-Dec;23(6):103-6. doi: 10.1377/hlthaff.23.6.103. PMID: 15537588.
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Casting the light of the dismal science on health care.

Health affairs (Project Hope)

[No authors listed]
PMID: 23213146
Health Aff (Millwood). 2012 Dec;31(12):2629. doi: 10.1377/hlthaff.2012.1266.

Harvard economist Joe Newhouse has played a leading role in almost every major study in health economics of the past several decades-starting with the famed RAND Health Insurance Experiment.

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Casting the light of the dismal science on health care. Health Aff (Millwood). 2012;31(12):2629doi: 10.1377/hlthaff.2012.1266.
(2012). Casting the light of the dismal science on health care. Health affairs (Project Hope), 31(12), 2629. https://doi.org/10.1377/hlthaff.2012.1266
"Casting the light of the dismal science on health care." Health affairs (Project Hope) vol. 31,12 (2012): 2629. doi: https://doi.org/10.1377/hlthaff.2012.1266
Casting the light of the dismal science on health care. Health Aff (Millwood). 2012 Dec;31(12):2629. doi: 10.1377/hlthaff.2012.1266. PMID: 23213146.
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In response.

Medical care

Austin PC, Reeves MJ.
PMID: 23685405
Med Care. 2013 Jul;51(7):633-5. doi: 10.1097/MLR.0b013e31829760b6.

No abstract available.

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Austin PC, Reeves MJ. In response. Med Care. 2013;51(7):633-5doi: 10.1097/MLR.0b013e31829760b6.
Austin, P. C., & Reeves, M. J. (2013). In response. Medical care, 51(7), 633-5. https://doi.org/10.1097/MLR.0b013e31829760b6
Austin, Peter C, and Reeves, Mathew J. "In response." Medical care vol. 51,7 (2013): 633-5. doi: https://doi.org/10.1097/MLR.0b013e31829760b6
Austin PC, Reeves MJ. In response. Med Care. 2013 Jul;51(7):633-5. doi: 10.1097/MLR.0b013e31829760b6. PMID: 23685405.
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Should ACA risk adjustment be more like Medicare Advantage?.

Modern healthcare

Herman B.
PMID: 30480880
Mod Healthc. 2016 Sep;46(34):8.

Insurers may think Medicare has a better risk-adjustment structure than the exchanges. Good luck convincing Congress.

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Herman B. Should ACA risk adjustment be more like Medicare Advantage?. Mod Healthc. 2016;46(34):8
Herman, B. (2016). Should ACA risk adjustment be more like Medicare Advantage?. Modern healthcare, 46(34), 8.
Herman, Bob. "Should ACA risk adjustment be more like Medicare Advantage?." Modern healthcare vol. 46,34 (2016): 8.
Herman B. Should ACA risk adjustment be more like Medicare Advantage?. Mod Healthc. 2016 Sep;46(34):8. PMID: 30480880.
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A Hot Health Policy Summer.

Health affairs (Project Hope)

Keith K.
PMID: 30199278
Health Aff (Millwood). 2018 Oct;37(10):1544-1545. doi: 10.1377/hlthaff.2018.1110. Epub 2018 Sep 10.

Last summer the administration finalized new rules on short-term plans and approved new state waivers. Litigation continues over the individual mandate, risk adjustment, and ACA "sabotage."

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Keith K. A Hot Health Policy Summer. Health Aff (Millwood). 2018;37(10):1544-1545doi: 10.1377/hlthaff.2018.1110.
Keith, K. (2018). A Hot Health Policy Summer. Health affairs (Project Hope), 37(10), 1544-1545. https://doi.org/10.1377/hlthaff.2018.1110
Keith, Katie. "A Hot Health Policy Summer." Health affairs (Project Hope) vol. 37,10 (2018): 1544-1545. doi: https://doi.org/10.1377/hlthaff.2018.1110
Keith K. A Hot Health Policy Summer. Health Aff (Millwood). 2018 Oct;37(10):1544-1545. doi: 10.1377/hlthaff.2018.1110. Epub 2018 Sep 10. PMID: 30199278.
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Assessing the performance of a method for case-mix adjustment in the Korean Diagnosis-Related Groups (KDRG) system and its policy implications.

Health research policy and systems

Kim S, Choi B, Lee K, Lee S, Kim S.
PMID: 34187515
Health Res Policy Syst. 2021 Jun 29;19(1):98. doi: 10.1186/s12961-021-00739-5.

BACKGROUND: To evaluate the performance of the patient clinical complexity level (PCCL) mechanism, which is the patient-level complexity adjustment factor within the Korean Diagnosis-Related Groups (KDRG) patient classification system, in explaining the variation in resource consumption within age adjacent...

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Kim S, Choi B, Lee K, et al. Assessing the performance of a method for case-mix adjustment in the Korean Diagnosis-Related Groups (KDRG) system and its policy implications. Health Res Policy Syst. 2021;19(1):98doi: 10.1186/s12961-021-00739-5.
Kim, S., Choi, B., Lee, K., Lee, S., & Kim, S. (2021). Assessing the performance of a method for case-mix adjustment in the Korean Diagnosis-Related Groups (KDRG) system and its policy implications. Health research policy and systems, 19(1), 98. https://doi.org/10.1186/s12961-021-00739-5
Kim, Sujeong, et al. "Assessing the performance of a method for case-mix adjustment in the Korean Diagnosis-Related Groups (KDRG) system and its policy implications." Health research policy and systems vol. 19,1 (2021): 98. doi: https://doi.org/10.1186/s12961-021-00739-5
Kim S, Choi B, Lee K, Lee S, Kim S. Assessing the performance of a method for case-mix adjustment in the Korean Diagnosis-Related Groups (KDRG) system and its policy implications. Health Res Policy Syst. 2021 Jun 29;19(1):98. doi: 10.1186/s12961-021-00739-5. PMID: 34187515; PMCID: PMC8243480.
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Rethinking standardised infection rates and risk adjustment in the COVID-19 era.

BMJ quality & safety

Salmasian H, Beloff J, Resnick A, Rhee C, Baker MA, Klompas M, Pimentel MP.
PMID: 33419786
BMJ Qual Saf. 2021 Jul;30(7):588-590. doi: 10.1136/bmjqs-2020-012074. Epub 2021 Jan 08.

No abstract available.

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Salmasian H, Beloff J, Resnick A, et al. Rethinking standardised infection rates and risk adjustment in the COVID-19 era. BMJ Qual Saf. 2021;30(7):588-590doi: 10.1136/bmjqs-2020-012074.
Salmasian, H., Beloff, J., Resnick, A., Rhee, C., Baker, M. A., Klompas, M., & Pimentel, M. P. (2021). Rethinking standardised infection rates and risk adjustment in the COVID-19 era. BMJ quality & safety, 30(7), 588-590. https://doi.org/10.1136/bmjqs-2020-012074
Salmasian, Hojjat, et al. "Rethinking standardised infection rates and risk adjustment in the COVID-19 era." BMJ quality & safety vol. 30,7 (2021): 588-590. doi: https://doi.org/10.1136/bmjqs-2020-012074
Salmasian H, Beloff J, Resnick A, Rhee C, Baker MA, Klompas M, Pimentel MP. Rethinking standardised infection rates and risk adjustment in the COVID-19 era. BMJ Qual Saf. 2021 Jul;30(7):588-590. doi: 10.1136/bmjqs-2020-012074. Epub 2021 Jan 08. PMID: 33419786.
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Risk Adjustment Instruments in Administrative Data Studies: A Primer for Neurosurgeons.

World neurosurgery

Kim LH, Chen YR.
PMID: 31048059
World Neurosurg. 2019 Aug;128:477-500. doi: 10.1016/j.wneu.2019.04.179. Epub 2019 Apr 30.

With the increasing interest in big data and health services research, use of administrative databases is becoming commonplace in health care studies, including in neurosurgery. Administrative data offer the unique advantage of accessing large amounts of information previously collected...

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Kim LH, Chen YR. Risk Adjustment Instruments in Administrative Data Studies: A Primer for Neurosurgeons. World Neurosurg. 2019;128:477-500doi: 10.1016/j.wneu.2019.04.179.
Kim, L. H., & Chen, Y. R. (2019). Risk Adjustment Instruments in Administrative Data Studies: A Primer for Neurosurgeons. World neurosurgery, 128477-500. https://doi.org/10.1016/j.wneu.2019.04.179
Kim, Lily H, and Chen, Yi-Ren. "Risk Adjustment Instruments in Administrative Data Studies: A Primer for Neurosurgeons." World neurosurgery vol. 128 (2019): 477-500. doi: https://doi.org/10.1016/j.wneu.2019.04.179
Kim LH, Chen YR. Risk Adjustment Instruments in Administrative Data Studies: A Primer for Neurosurgeons. World Neurosurg. 2019 Aug;128:477-500. doi: 10.1016/j.wneu.2019.04.179. Epub 2019 Apr 30. PMID: 31048059.
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Risk Adjustment Methods in Quality Measurement-Reply.

JAMA

Braithwaite RS.
PMID: 30326122
JAMA. 2018 Oct 16;320(15):1605. doi: 10.1001/jama.2018.11519.

No abstract available.

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Braithwaite RS. Risk Adjustment Methods in Quality Measurement-Reply. JAMA. 2018;320(15):1605doi: 10.1001/jama.2018.11519.
Braithwaite, R. S. (2018). Risk Adjustment Methods in Quality Measurement-Reply. JAMA, 320(15), 1605. https://doi.org/10.1001/jama.2018.11519
Braithwaite, R Scott. "Risk Adjustment Methods in Quality Measurement-Reply." JAMA vol. 320,15 (2018): 1605. doi: https://doi.org/10.1001/jama.2018.11519
Braithwaite RS. Risk Adjustment Methods in Quality Measurement-Reply. JAMA. 2018 Oct 16;320(15):1605. doi: 10.1001/jama.2018.11519. PMID: 30326122.
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Showing 1 to 12 of 40 entries