Publications by authors named "Shannon M E Murphy"

9 Publications

  • Page 1 of 1

Utilization of Medical Codes for Hypotension in Shock Patients: A Retrospective Analysis.

J Multidiscip Healthc 2021 19;14:861-867. Epub 2021 Apr 19.

Boston Strategic Partners, Inc., Boston, MA, USA.

Purpose: To evaluate the utilization of hypotension diagnosis codes by shock type and year in known hypotensive patients.

Patients And Methods: Retrospective analysis of the Medicare fee-for-service claims database. Patients with a shock diagnosis code between 2011 and 2017 were identified using the International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification (ICD-9-CM and ICD-10-CM). Based on specific ICD codes corresponding to each shock type, patients were classified into four mutually exclusive cohorts: cardiogenic shock, hypovolemic shock, septic shock, and other/unspecified shock. Annual proportion and counts of cases with at least one hypotension ICD code for each shock cohort were generated to produce 7-year medical code utilization trends. A Cochran-Armitage test for trend was performed to evaluate the statistical significance.

Results: A total of 2,200,275 shock patients were analyzed, 13.3% (n=292,192) of which received a hypotension code. Hypovolemic shock cases were the most likely to receive a hypotension code (18.02%, n=46,544), while septic shock cases had the lowest rate (11.48%, n=158,348). The proportion of patients with hypotension codes for other cohorts were 18.0% (n=46,544) for hypovolemic shock and 16.9% (n=32,024) for other/unspecified shock. The presence of hypotension codes decreased by 0.9% between 2011 and 2014, but significantly increased from 10.6% in 2014 to 17.9% in 2017 (p <0.0001, Z=-105.05).

Conclusion: Hypotension codes are remarkably underutilized in known hypotensive patients. Patients, providers, and researchers are likely to benefit from improved hypotension coding practices.
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http://dx.doi.org/10.2147/JMDH.S305985DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8064679PMC
April 2021

Relation of Institutional Mitral Valve Surgical Volume to Surgical and Transcatheter Outcomes in Medicare Patients.

Am J Cardiol 2021 05 2;147:94-100. Epub 2021 Mar 2.

Edwards Lifesciences, Irvine, CA, USA.

There are limited data to support proposed increases to the minimum institutional mitral valve (MV) surgery volume required to begin a transcatheter mitral valve repair (TMVr) program. The current study examined the association between institutional MV procedure volumes and outcomes. All 2017 Medicare fee-for-service patients who received a TMVr or MV surgery procedure were included and analyzed separately. The exposure was institutional MV surgery volume: low (1 to 24), medium (25 to 39) or high (40+). Outcomes were in-hospital mortality and 1-year postdischarge mortality and cardiovascular rehospitalization. For MV surgery patients, in-hospital mortality rates were 6.4% at low-volume, 8.7% at medium-volume and 9.8% at high-volume facilities. Rates were significantly higher for low-volume [OR = 1.50, 95% CI (1.23 to 1.84)] and medium-volume [OR = 1.33, 95% CI (1.06 to 1.67)] compared with high-volume facilities. There was no statistically significant relationship between institutional MV surgery volume and in-hospital mortality for TMVr patients, either at low-volume [OR = 1.52, 95% CI (0.56, 4.13)] or medium-volume [OR = 1.58, 95% CI (0.82, 3.02)] facilities, compared with high-volume facilities. Across all volume categories, in-hospital mortality rates for TMVr patients were relatively low (2.3% on average). For both cohorts, the rates of 1-year mortality and cardiovascular rehospitalizations were not significantly higher at low- or medium-volume MV surgery facilities, as compared with high-volume. In conclusion, among Medicare patients, there was a relation between institutional MV surgery volume and in-hospital mortality for MV surgery patients, but not for TMVr patients.
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http://dx.doi.org/10.1016/j.amjcard.2021.01.044DOI Listing
May 2021

Association of a Care Coordination Model With Health Care Costs and Utilization: The Johns Hopkins Community Health Partnership (J-CHiP).

JAMA Netw Open 2018 11 2;1(7):e184273. Epub 2018 Nov 2.

Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Importance: The Johns Hopkins Community Health Partnership was created to improve care coordination across the continuum in East Baltimore, Maryland.

Objective: To determine whether the Johns Hopkins Community Health Partnership (J-CHiP) was associated with improved outcomes and lower spending.

Design, Setting, And Participants: Nonrandomized acute care intervention (ACI) and community intervention (CI) Medicare and Medicaid participants were analyzed in a quality improvement study using difference-in-differences designs with propensity score-weighted and matched comparison groups. The study spanned 2012 to 2016 and took place in acute care hospitals, primary care clinics, skilled nursing facilities, and community-based organizations. The ACI analysis compared outcomes of participants in Medicare and Medicaid during their 90-day postacute episode with those of a propensity score-weighted preintervention group at Johns Hopkins Community Health Partnership hospitals and a concurrent comparison group drawn from similar Maryland hospitals. The CI analysis compared changes in outcomes of Medicare and Medicaid participants with those of a propensity score-matched comparison group of local residents.

Interventions: The ACI bundle aimed to improve transition planning following discharge. The CI included enhanced care coordination and integrated behavioral support from local primary care sites in collaboration with community-based organizations.

Main Outcomes And Measures: Utilization measures of hospital admissions, 30-day readmissions, and emergency department visits; quality of care measures of potentially avoidable hospitalizations, practitioner follow-up visits; and total cost of care (TCOC) for Medicare and Medicaid participants.

Results: The CI group had 2154 Medicare beneficiaries (1320 [61.3%] female; mean age, 69.3 years) and 2532 Medicaid beneficiaries (1483 [67.3%] female; mean age, 55.1 years). For the CI group's Medicaid participants, aggregate TCOC reduction was $24.4 million, and reductions of hospitalizations, emergency department visits, 30-day readmissions, and avoidable hospitalizations were 33, 51, 36, and 7 per 1000 beneficiaries, respectively. The ACI group had 26 144 beneficiary-episodes for Medicare (13 726 [52.5%] female patients; mean patient age, 68.4 years) and 13 921 beneficiary-episodes for Medicaid (7392 [53.1%] female patients; mean patient age, 52.2 years). For the ACI group's Medicare participants, there was a significant reduction in aggregate TCOC of $29.2 million with increases in 90-day hospitalizations and 30-day readmissions of 11 and 14 per 1000 beneficiary-episodes, respectively, and reduction in practitioner follow-up visits of 41 and 29 per 1000 beneficiary-episodes for 7-day and 30-day visits, respectively. For the ACI group's Medicaid participants, there was a significant reduction in aggregate TCOC of $59.8 million and the 90-day emergency department visit rate decreased by 133 per 1000 episodes, but hospitalizations increased by 49 per 1000 episodes and practitioner follow-up visits decreased by 70 and 182 per 1000 episodes for 7-day and 30-day visits, respectively. In total, the CI and ACI were associated with $113.3 million in cost savings.

Conclusions And Relevance: A care coordination model consisting of complementary bundled interventions in an urban academic environment was associated with lower spending and improved health outcomes.
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http://dx.doi.org/10.1001/jamanetworkopen.2018.4273DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6324376PMC
November 2018

Incidence of Lyme Disease Diagnosis in a Maryland Medicaid Population, 2004-2011.

Am J Epidemiol 2018 10;187(10):2202-2209

Division of Rheumatology, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland.

The epidemiology of Lyme disease has been examined utilizing insurance claims from privately insured individuals; however, it is unknown whether reported patterns vary among the publicly insured. We examined trends in incidence rates of first Lyme disease diagnosis among 384,652 Maryland Medicaid recipients enrolled from July 2004 to June 2011. Age-, sex-, county-, season-, and year-specific incidence rates were calculated, and mixed-effects multiple logistic regression models were used to study the relationship between Lyme disease diagnosis and these variables. The incidence rate in our sample was 97.65 cases per 100,000 person-years (95% confidence interval (CI): 91.53, 104.06), and there was a 13% average annual increase in the odds of a Lyme disease diagnosis (odds ratio = 1.13, 95% CI: 1.09, 1.17; P < 0.001). Incidence rates for males and females were not significantly different, though males were significantly more likely to be diagnosed during high-season months (relative risk (RR) = 1.24, 95% CI: 1.06, 1.44) and less likely to be diagnosed during low-season months (RR = 0.63, 95% CI: 0.46, 0.87) than females. Additionally, adults were significantly more likely than children to be diagnosed during low-season months (RR = 1.59, 95% CI: 1.19, 2.12). While relatively rare in this study sample, Lyme disease diagnoses do occur in a Medicaid population in a Lyme-endemic state.
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http://dx.doi.org/10.1093/aje/kwy133DOI Listing
October 2018

Going Beyond Clinical Care to Reduce Health Care Spending: Findings From the J-CHiP Community-based Population Health Management Program Evaluation.

Med Care 2018 07;56(7):603-609

Johns Hopkins University Carey Business School, Baltimore, MD.

Background: Addressing both clinical and nonclinical determinants of health is essential for improving population health outcomes. In 2012, the Johns Hopkins Community Health Partnership (J-CHiP) implemented innovative population health management programs across acute and community environments. The community-based program involved multidisciplinary teams [ie, physicians, care managers (CM), health behavior specialists (HBS), community health workers, neighborhood navigators] and collaboration with community-based organizations to address social determinants.

Objectives: To report the impact of a community-based program on cost and utilization from 2011 to 2016.

Design: Difference-in-difference estimates were calculated for an inclusive cohort of J-CHiP participants and matched nonparticipants. The analysis was replicated for participants with a CM and/or HBS to estimate the differential impact with more intensive program services.

Subjects: A total of 3268 high-risk Medicaid and Medicare beneficiaries (1634 total J-CHiP participants, 1365 with CM and 678 with HBS).

Outcome Measures: Paid costs and counts of emergency department visits, admissions, and readmissions per member per year.

Results: For Medicaid, costs were almost $1200 per member per year lower for participants as a whole, $2000 lower for those with an HBS, and $3000 lower for those with a CM; hospital admission and readmission rates were 9%-26% lower for those with a CM and/or HBS. For Medicare, costs were lower (-$476), but utilization was similar or higher than nonparticipants. None of the observed Medicaid or Medicare differences were statistically significant.

Conclusions: Although not statistically significant, the results indicate a promising innovation for Medicaid beneficiaries. For Medicare, the impact was negligible, indicating the need for further program modification.
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http://dx.doi.org/10.1097/MLR.0000000000000934DOI Listing
July 2018

Key Design Considerations When Calculating Cost Savings for Population Health Management Programs in an Observational Setting.

Health Serv Res 2018 08 8;53 Suppl 1:3107-3124. Epub 2018 Feb 8.

Johns Hopkins University Carey Business School, Baltimore, MD.

Objective: To illustrate the impact of key quasi-experimental design elements on cost savings measurement for population health management (PHM) programs.

Data Sources: Population health management program records and Medicaid claims and enrollment data from December 2011 through March 2016.

Study Design: The study uses a difference-in-difference design to compare changes in cost and utilization outcomes between program participants and propensity score-matched nonparticipants. Comparisons of measured savings are made based on (1) stable versus dynamic population enrollment and (2) all eligible versus enrolled-only participant definitions. Options for the operationalization of time are also discussed.

Data Collection/extraction Methods: Individual-level Medicaid administrative and claims data and PHM program records are used to match study groups on baseline risk factors and assess changes in costs and utilization.

Principal Findings: Savings estimates are statistically similar but smaller in magnitude when eliminating variability based on duration of population enrollment and when evaluating program impact on the entire target population. Measurement in calendar time, when possible, simplifies interpretability.

Conclusion: Program evaluation design elements, including population stability and participant definitions, can influence the estimated magnitude of program savings for the payer and should be considered carefully. Time specifications can also affect interpretability and usefulness.
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http://dx.doi.org/10.1111/1475-6773.12832DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6056594PMC
August 2018

A method for estimating cost savings for population health management programs.

Health Serv Res 2013 Apr 27;48(2 Pt 1):582-602. Epub 2012 Aug 27.

Research and Development, Johns Hopkins HealthCare LLC, Glen Burnie, MD 21060, USA.

Objective: To develop a quasi-experimental method for estimating Population Health Management (PHM) program savings that mitigates common sources of confounding, supports regular updates for continued program monitoring, and estimates model precision.

Data Sources: Administrative, program, and claims records from January 2005 through June 2009.

Data Collection/extraction Methods: Data are aggregated by member and month.

Study Design: Study participants include chronically ill adult commercial health plan members. The intervention group consists of members currently enrolled in PHM, stratified by intensity level. Comparison groups include (1) members never enrolled, and (2) PHM participants not currently enrolled. Mixed model smoothing is employed to regress monthly medical costs on time (in months), a history of PHM enrollment, and monthly program enrollment by intensity level. Comparison group trends are used to estimate expected costs for intervention members. Savings are realized when PHM participants' costs are lower than expected.

Principal Findings: This method mitigates many of the limitations faced using traditional pre-post models for estimating PHM savings in an observational setting, supports replication for ongoing monitoring, and performs basic statistical inference.

Conclusion: This method provides payers with a confident basis for making investment decisions.
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http://dx.doi.org/10.1111/j.1475-6773.2012.01457.xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3626355PMC
April 2013

Predictive modeling in practice: improving the participant identification process for care management programs using condition-specific cut points.

Popul Health Manag 2011 Aug 17;14(4):205-10. Epub 2011 Jan 17.

Research and Development Unit, Johns Hopkins HealthCare LLC, Glen Burnie, Maryland 21060, USA.

The objective of this study was to optimize predictive modeling in the participant selection process for care management (CM) programs by determining the ideal cut point selection method. Comparisons included: (a) an evidence-based "optimal" cut point versus an "arbitrary" threshold, and (b) condition-specific cut points versus a uniform screening method. Participants comprised adult Medicaid health plan members enrolled during the entire study period (January 2007-December 2008) who had at least 1 of the chronic conditions targeted by the CM programs (n = 6459). Adjusted Clinical Groups Predictive Modeling (ACG-PM) system risk scores in 2007 were used to predict those with the top 5% highest health care expenditures in 2008. Comparisons of model performance (ie, c statistic, sensitivity, specificity, positive predictive value) and identified population size were used to assess differences among 3 cut point selection approaches: (a) single arbitrary cut point, (b) single optimal cut point, and (c) condition-specific optimal cut points. The "optimal" cut points (ie, single and condition-specific) both outperformed the "arbitrary" selection process, yielding higher probabilities of correct prediction and sensitivities. The condition-specific optimal cut point approach also exhibited better performance than applying a single optimal cut point uniformly across the entire population regardless of condition (ie, a higher c statistic, specificity, and positive predictive value, although sensitivity was lower), while identifying a more manageable number of members for CM program outreach. CM programs can optimize targeting algorithms by utilizing evidence-based cut points that incorporate condition-specific variations in risk. By efficiently targeting and intervening with future high-cost members, health care costs can be reduced.
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http://dx.doi.org/10.1089/pop.2010.0005DOI Listing
August 2011

Chronic care improvement in primary care: evaluation of an integrated pay-for-performance and practice-based care coordination program among elderly patients with diabetes.

Health Serv Res 2010 Dec 17;45(6 Pt 1):1763-82. Epub 2010 Sep 17.

Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Johns Hopkins Healthcare LLC, 6704 Curtis Court, Glen Burnie, MD 21060, USA.

Objective: To examine the effects of an intervention comprising (1) a practice-based care coordination program, (2) augmented by pay for performance (P4P) for meeting quality targets, and (3) complemented by a third-party disease management on quality of care and resource use for older adults with diabetes.

Data Sources/study Setting: Claims files of a managed care organization (MCO) for 20,943 adults aged 65 and older with diabetes receiving care in Alabama, Tennessee, or Texas, from January 2004 to March 2007.

Study Design: A quasi-experimental, longitudinal study in which pre- and postdata from 1,587 patients in nine intervention primary care practices were evaluated against 19,356 patients in MCO comparison practices (>900). Five incentivized quality measures, two nonincentivized measures, and two resource-use measures were investigated. We examined trends and changes in trends from baseline to follow-up, contrasting intervention and comparison group member results.

Principal Findings: Quality of care generally improved for both groups during the study period. Only slight differences were seen between the intervention and comparison group trends and changes in trends over time.

Conclusions: This study did not generate evidence supporting a beneficial effect of an on-site care coordination intervention augmented by P4P and complemented by third-party disease management on diabetes quality or resource use.
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http://dx.doi.org/10.1111/j.1475-6773.2010.01166.xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3026957PMC
December 2010
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