Publications by authors named "John Billings"

45 Publications

Impact of a Primary Care Provider Tele-Mentoring and Community Health Worker Intervention on Utilization in Medicaid Patients with Diabetes.

Endocr Pract 2020 Oct;26(10):1070-1076

Wagner School of Public Service, New York University, New York, New York.

Objective: The Endocrinology ECHO intervention utilized a tele-mentoring model that connects primary care providers (PCPs) and community health workers (CHWs) with specialists for training in diabetes care. We evaluated the impact of the Endo ECHO intervention on healthcare utilization and care for Medicaid patients with diabetes in New Mexico.

Methods: Between January 2015 and April 2017, patients with complex diabetes from 10 health centers in NM were recruited to receive diabetes care from a PCP and CHW upskilled through Endo ECHO. We matched intervention patients in the NM Medicaid claims database to comparison Medicaid beneficiaries using 5:1 propensity matching. We used a difference-in-difference (DID) approach to compare utilization and processes of care between intervention and comparison patients.

Results: Of 541 Medicaid patients enrolled in Endo ECHO, 305 met inclusion criteria and were successfully matched. Outpatient visits increased with Endo ECHO for intervention patients as compared to comparison patients (rate ratio, 1.57; 95% confidence interval [CI], 1.43 to 1.72). The intervention was associated with an increase in emergency department (ED) visits (rate ratio, 1.30; 95% CI, 1.04 to 1.63) but no change in hospitalizations (rate ratio, 1.47; 95% CI, 0.95 to 2.23). Among intervention patients, utilization of metformin increased from 57.1% to 60.7%, with a DID between groups of 8.8% (95% CI, 4.0% to 13.6%). We found similar increases in use of statins (DID, 8.5%; 95% CI, 3.2% to 13.8%), angiotensin-converting enzyme inhibitors (DID, 9.5%; 95% CI, 3.5% to 15.4%), or antidepressant therapies (DID, 9.4%; 95% CI, 1.1% to 18.1%).

Conclusion: Patient enrollment in Endo ECHO was associated with increased outpatient and ED utilization and increased uptake of prescription-related quality measures. No impact was observed on hospitalization.
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http://dx.doi.org/10.4158/EP-2019-0535DOI Listing
October 2020

A Telementoring Intervention Leads to Improvements in Self-Reported Measures of Health Care Access and Quality among Patients with Complex Diabetes.

J Health Care Poor Underserved 2020 ;31(3):1124-1133

Individuals living with complex diabetes experience limited access to endocrine care due to a nationwide shortage of endocrinologists. Project ECHO (Extension for Community Healthcare Outcomes) is an innovative, scalable model of health care that extends specialty care to medically underserved areas through ongoing telementorship of community primary care providers. We evaluated the effects of an endocrine-focused ECHO program (Endo ECHO) on patients with type 1 and complex type 2 diabetes, and report here on changes in patient-reported measures of health care access and quality from baseline to one year aft er program enrollment. Patients were eligible for Endo ECHO if they were 18 years or older with complex diabetes. Aft er participating in Endo ECHO, access to health care and diabetes-related quality of care improved dramatically. Our results suggest that Endo ECHO may be a suitable intervention for extending best practices in diabetes care to medically underserved patients.
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http://dx.doi.org/10.1353/hpu.2020.0085DOI Listing
January 2020

The Quality of Medical Evidence and Medical Practice: March 1987.

Am J Ophthalmol 2021 05 26;225:189-205. Epub 2020 Aug 26.

Center for Health Policy Research and Education, Duke University, Durham, North Carolina.

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http://dx.doi.org/10.1016/j.ajo.2020.08.034DOI Listing
May 2021

IMPACT OF A PRIMARY CARE PROVIDER (PCP) TELE-MENTORING AND COMMUNITY HEALTH WORKER INTERVENTION ON UTILIZATION IN MEDICAID PATIENTS WITH DIABETES.

Endocr Pract 2020 Jun 23. Epub 2020 Jun 23.

Wagner School of Public Service, New York University, New York, NY.

Background: The Endocrinology ECHO intervention utilized a tele-mentoring model that connects primary care providers (PCPs) and community health workers (CHWs) with specialists for training in diabetes care. We evaluated the impact of the Endo ECHO intervention on healthcare utilization and care for Medicaid patients with diabetes in New Mexico (NM).

Methods: Between January 2015 and April 2017, patients with complex diabetes from 10 health centers in NM were recruited to receive diabetes care from a PCP and CHW upskilled through Endo ECHO. We matched intervention patients in the NM Medicaid claims database to comparison Medicaid beneficiaries using 5:1 propensity matching. We used a difference-in-difference (DID) approach to compare utilization and processes of care between intervention and comparison patients.

Results: Of 541 Medicaid patients enrolled in Endo ECHO, 305 met inclusion criteria and were successfully matched. Outpatient visits increased with Endo ECHO for intervention patients as compared to comparison patients (rate ratio 1.57; 95% CI 1.43-1.72). The intervention was associated with an increase in ED visits (rate ratio 1.30; 95% CI 1.04-1.63) but no change in hospitalizations (rate ratio 1.47; 95% CI 0.95-2.23). Among intervention patients, utilization of metformin increased from 57.1% to 60.7%, with a DID between groups of 8.8% (95% CI 4.0%-13.6%). We found similar increases in use of statins (DID=8.5%, 95% CI 3.2%-13.8%), ACE inhibitors (DID=9.5%, 95% CI 3.5%-15.4%), or anti-depressant therapies (DID=9.4%, 95% CI 1.1%-18.1%).

Discussion: Patient enrollment in Endo ECHO was associated with increased outpatient and ED utilization and increased uptake of prescription-related quality measures. No impact was observed on hospitalization.
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http://dx.doi.org/10.4158/EP-2019-0535DOI Listing
June 2020

Study protocol for a pragmatic trial of the Consult for Addiction Treatment and Care in Hospitals (CATCH) model for engaging patients in opioid use disorder treatment.

Addict Sci Clin Pract 2019 02 19;14(1). Epub 2019 Feb 19.

Office of Behavioral Health, NYC Health + Hospitals, 125 Worth St, New York, NY, 10013, USA.

Background: Treatment for opioid use disorder (OUD) is highly effective, yet it remains dramatically underutilized. Individuals with OUD have disproportionately high rates of hospitalization and low rates of addiction treatment. Hospital-based addiction consult services offer a potential solution by using multidisciplinary teams to evaluate patients, initiate medication for addiction treatment (MAT) in the hospital, and connect patients to post-discharge care. We are studying the effectiveness of an addiction consult model [Consult for Addiction Treatment and Care in Hospitals (CATCH)] as a strategy for engaging patients with OUD in treatment as the program rolls out in the largest municipal hospital system in the US. The primary aim is to evaluate the effectiveness of CATCH in increasing post-discharge initiation and engagement in MAT. Secondary aims are to assess treatment retention, frequency of acute care utilization and overdose deaths and their associated costs, and implementation outcomes.

Methods: A pragmatic trial at six hospitals, conducted in collaboration with the municipal hospital system and department of health, will be implemented to study the CATCH intervention. Guided by the RE-AIM evaluation framework, this hybrid effectiveness-implementation study (Type 1) focuses primarily on effectiveness and also measures implementation outcomes to inform the intervention's adoption and sustainability. A stepped-wedge cluster randomized trial design will determine the impact of CATCH on treatment outcomes in comparison to usual care for a control period, followed by a 12-month intervention period and a 6- to 18-month maintenance period at each hospital. A mixed methods approach will primarily utilize administrative data to measure outcomes, while interviews and focus groups with staff and patients will provide additional information on implementation fidelity and barriers to delivering MAT to patients with OUD.

Discussion: Because of their great potential to reduce the negative health and economic consequences of untreated OUD, addiction consult models are proliferating in response to the opioid epidemic, despite the absence of a strong evidence base. This study will provide the first known rigorous evaluation of an addiction consult model in a large multi-site trial and promises to generate knowledge that can rapidly transform practice and inform the potential for widespread dissemination of these services.

Trial Registration: NCT03611335.
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http://dx.doi.org/10.1186/s13722-019-0135-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6380041PMC
February 2019

The "sigmoid" esophagus.

Abdom Radiol (NY) 2019 05;44(5):1944-1945

Department of Radiology, Wake Forest Baptist Medical Center, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, 27157, USA.

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http://dx.doi.org/10.1007/s00261-019-01902-xDOI Listing
May 2019

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

Neuroimaging Clin N Am 2017 Nov;27(4):609-620

Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA; Division of Neuroradiology, Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA; Department of Biomedical Engineering, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA; Clinical and Translational Sciences Institute (CTSI), Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA. Electronic address:

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances.
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http://dx.doi.org/10.1016/j.nic.2017.06.010DOI Listing
November 2017

Preventing hospital readmissions: the importance of considering 'impactibility,' not just predicted risk.

BMJ Qual Saf 2017 10 14;26(10):782-785. Epub 2017 Jun 14.

NYU Wagner, New York University, New York, New York, USA.

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http://dx.doi.org/10.1136/bmjqs-2017-006629DOI Listing
October 2017

Taking Telemedicine to the Next Level in Diabetes Population Management: a Review of the Endo ECHO Model.

Curr Diab Rep 2016 10;16(10):96

Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA.

Worldwide increases in diabetes prevalence in the face of limited medical resources have prompted international interest in innovative healthcare delivery models. Project ECHO (Extension for Community Healthcare Outcomes) is a "telementoring" program which has been shown to increase capacity for complex disease management in medically underserved regions. In contrast to a traditional telemedicine model which might connect a specialist with one patient, the ECHO model allows for multiple patients to benefit simultaneously by building new expertise. We recently applied the ECHO model to improve health outcomes of patients with complex diabetes (Endo ECHO) living in rural New Mexico. We describe the design of the Endo ECHO intervention and a 4-year, prospective program evaluation assessing health outcomes, utilization patterns, and cost-effectiveness. The Endo ECHO evaluation will demonstrate whether and to what extent this intervention improves outcomes for patients with complex diabetes living in rural New Mexico, and will serve as proof-of-concept for academic medical centers wishing to replicate the model in underserved regions around the world.
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http://dx.doi.org/10.1007/s11892-016-0784-9DOI Listing
October 2016

Factors associated with variation in hospital use at the end of life in England.

BMJ Support Palliat Care 2019 Jun 24;9(2):167-174. Epub 2016 Mar 24.

Robert F. Wagner Graduate School of Public Service, New York University, New York, New York, USA.

Objective: To identify the relative importance of factors influencing hospital use at the end of life.

Design: Retrospective cohort study of person and health system effects on hospital use in the past 12 months modelling differences in admissions, bed days and whether a person died in hospital.

Setting: Residents in England for the period 2009/2010 to 2011/2012 using Hospital Episodes Statistics (HES) data from all acute care hospitals in England funded by the National Health Service (NHS).

Participants: 1 223 859 people registered with a GP in England who died (decedents) in England (April 2009-March 2012) with a record of NHS hospital care.

Main Outcome Measures: Hospital admissions, and hospital bed days and place of death (in or out of hospital) in the past 12 months of life.

Results: The mean number of admissions in the past 12 months of life averaged 2.28 occupying 30.05 bed days-excluding 9.8% of patients with no hospital history. A total of 50.8% of people died in hospital. Difference in hospital use was associated with a range of patient descriptors (age, gender and ethnicity). The variables with the greatest 'explanatory power' were those that described the diagnoses and causes of death. So, for example, 65% of the variability in the model of hospital admissions was explained by diagnoses. Only moderate levels of variation were explained by the hospital provider variables for admissions and deaths in hospital, though the impacts on total bed days was large.

Conclusions: Comparative analyses of hospital utilisation should standardise for a range of patient specific variables. Though the models indicated some degree of variability associated with individual providers, the scale of this was not great for admissions and death in hospital but the variability associated with length of stay differences suggests that attempts to optimise hospital use should look at differences in lengths of stay and bed use. This study adds important new information about variability in admissions by diagnostic group, and variability in bed days by diagnostic group and eventual cause of death.
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http://dx.doi.org/10.1136/bmjspcare-2015-000936DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6582820PMC
June 2019

A pilot study of implantable cardiac device interrogation by emergency department personnel.

Crit Pathw Cardiol 2014 Mar;13(1):6-8

From the *Department of Emergency Medicine, Ohio State University Medical Center, Columbus, OH; †Department of Emergency Medicine, Wake Forest University Health Sciences, Winston-Salem, NC; ‡Department of Emergency Medicine, Baylor College of Medicine, Houston, TX; §Department of Radiology, Wake Forest University Health Sciences, Winston-Salem, NC; ¶Department of Emergency Medicine, Sunrise Hospital, Las Vegas, NV; and ‖Division of Cardiovascular Medicine, Ohio State University Medical Center, Columbus, OH.

Background: Implanted devices (eg, pacemakers and defibrillators) provide valuable information and may be interrogated to obtain diagnostic information and to direct management. During admission to an emergency department (ED), significant time and cost are spent waiting for device manufacturer representatives or cardiologists to access the data. If ED personnel could safely interrogate implanted devices, more rapid disposition could occur, thus leading to potentially better outcomes at a reduced cost. This was a pilot study examining the feasibility of ED device interrogation.

Methods: This was a prospective convenience sample study of patients presenting to the ED with any chief complaint and who had an implantable device capable of being interrogated by a Medtronic reader. After obtaining informed consent, study patients underwent device interrogation by ED research personnel. After reviewing the device data, the physician documented their opinions of the value of data in aiding care. Patients were followed up at intervals ranging from 30 days out to 1 year to determine adverse events relating to interrogation.

Results: Forty-four patients underwent device interrogation. Their mean age was 56 ± 14.7 years (range, 28-83), 75% (33/44) were male and 75% (33/44) were hospitalized from the ED. The interrogations took less than 10 minutes 89% of the time. In 60% of the cases, ED physicians reported the data-assisted patient care. No adverse events were reported relating to the ED interrogations.

Conclusions: In this pilot study, we found that ED personnel can safely and quickly interrogate implantable devices to obtain potentially useful clinical data.
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http://dx.doi.org/10.1097/HPC.0000000000000000DOI Listing
March 2014

Emergency department use: the authors reply.

Health Aff (Millwood) 2014 Feb;33(2):346

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http://dx.doi.org/10.1377/hlthaff.2013.1461DOI Listing
February 2014

Dispelling an urban legend: frequent emergency department users have substantial burden of disease.

Health Aff (Millwood) 2013 Dec;32(12):2099-108

Urban legend has often characterized frequent emergency department (ED) patients as mentally ill substance users who are a costly drain on the health care system and who contribute to ED overcrowding because of unnecessary visits for conditions that could be treated more efficiently elsewhere. This study of Medicaid ED users in New York City shows that behavioral health conditions are responsible for a small share of ED visits by frequent users, and that ED use accounts for a small portion of these patients' total Medicaid costs. Frequent ED users have a substantial burden of disease, and they have high rates of primary and specialty care use. They also have linkages to outpatient care that are comparable to those of other ED patients. It is possible to use predictive modeling to identify who will become a repeat ED user and thus to help target interventions. However, policy makers should view reducing frequent ED use as only one element of more-comprehensive intervention strategies for frequent health system users.
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http://dx.doi.org/10.1377/hlthaff.2012.1276DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4892700PMC
December 2013

Choosing a model to predict hospital admission: an observational study of new variants of predictive models for case finding.

BMJ Open 2013 Aug 26;3(8):e003352. Epub 2013 Aug 26.

Robert F Wagner Graduate School of Public Service, New York University, New York, New York, USA.

Objectives: To test the performance of new variants of models to identify people at risk of an emergency hospital admission. We compared (1) the impact of using alternative data sources (hospital inpatient, A&E, outpatient and general practitioner (GP) electronic medical records) (2) the effects of local calibration on the performance of the models and (3) the choice of population denominators.

Design: Multivariate logistic regressions using person-level data adding each data set sequentially to test value of additional variables and denominators.

Setting: 5 Primary Care Trusts within England.

Participants: 1 836 099 people aged 18-95 registered with GPs on 31 July 2009.

Main Outcome Measures: Models to predict hospital admission and readmission were compared in terms of the positive predictive value and sensitivity for various risk strata and with the receiver operating curve C statistic.

Results: The addition of each data set showed moderate improvement in the number of patients identified with little or no loss of positive predictive value. However, even with inclusion of GP electronic medical record information, the algorithms identified only a small number of patients with no emergency hospital admissions in the previous 2 years. The model pooled across all sites performed almost as well as the models calibrated to local data from just one site. Using population denominators from GP registers led to better case finding.

Conclusions: These models provide a basis for wider application in the National Health Service. Each of the models examined produces reasonably robust performance and offers some predictive value. The addition of more complex data adds some value, but we were unable to conclude that pooled models performed less well than those in individual sites. Choices about model should be linked to the intervention design. Characteristics of patients identified by the algorithms provide useful information in the design/costing of intervention strategies to improve care coordination/outcomes for these patients.
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http://dx.doi.org/10.1136/bmjopen-2013-003352DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753475PMC
August 2013

Effect of telecare on use of health and social care services: findings from the Whole Systems Demonstrator cluster randomised trial.

Age Ageing 2013 Jul 25;42(4):501-8. Epub 2013 Feb 25.

The Nuffield Trust, London, UK.

Objective: to assess the impact of telecare on the use of social and health care. Part of the evaluation of the Whole Systems Demonstrator trial.

Participants And Setting: a total of 2,600 people with social care needs were recruited from 217 general practices in three areas in England.

Design: a cluster randomised trial comparing telecare with usual care, general practice being the unit of randomisation. Participants were followed up for 12 months and analyses were conducted as intention-to-treat.

Data Sources: trial data were linked at the person level to administrative data sets on care funded at least in part by local authorities or the National Health Service.

Main Outcome Measures: the proportion of people admitted to hospital within 12 months. Secondary endpoints included mortality, rates of secondary care use (seven different metrics), contacts with general practitioners and practice nurses, proportion of people admitted to permanent residential or nursing care, weeks in domiciliary social care and notional costs.

Results: 46.8% of intervention participants were admitted to hospital, compared with 49.2% of controls. Unadjusted differences were not statistically significant (odds ratio: 0.90, 95% CI: 0.75-1.07, P = 0.211). They reached statistical significance after adjusting for baseline covariates, but this was not replicated when adjusting for the predictive risk score. Secondary metrics including impacts on social care use were not statistically significant.

Conclusions: telecare as implemented in the Whole Systems Demonstrator trial did not lead to significant reductions in service use, at least in terms of results assessed over 12 months.
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http://dx.doi.org/10.1093/ageing/aft008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3684109PMC
July 2013

Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30).

BMJ Open 2012 10;2(4). Epub 2012 Aug 10.

Robert F. Wagner Graduate School of Public Service, New York University, New York, USA.

Objectives: To develop an algorithm for identifying inpatients at high risk of re-admission to a National Health Service (NHS) hospital in England within 30 days of discharge using information that can either be obtained from hospital information systems or from the patient and their notes.

Design: Multivariate statistical analysis of routinely collected hospital episode statistics (HES) data using logistic regression to build the predictive model. The model's performance was calculated using bootstrapping.

Setting: HES data covering all NHS hospital admissions in England.

Participants: The NHS patients were admitted to hospital between April 2008 and March 2009 (10% sample of all admissions, n=576 868).

Main Outcome Measures: Area under the receiver operating characteristic curve for the algorithm, together with its positive predictive value and sensitivity for a range of risk score thresholds.

Results: The algorithm produces a 'risk score' ranging (0-1) for each admitted patient, and the percentage of patients with a re-admission within 30 days and the mean re-admission costs of all patients are provided for 20 risk bands. At a risk score threshold of 0.5, the positive predictive value (ie, percentage of inpatients identified as high risk who were subsequently re-admitted within 30 days) was 59.2% (95% CI 58.0% to 60.5%); representing 5.4% (95% CI 5.2% to 5.6%) of all inpatients who would be re-admitted within 30 days (sensitivity). The area under the receiver operating characteristic curve was 0.70 (95% CI 0.69 to 0.70).

Conclusions: We have developed a method of identifying inpatients at high risk of unplanned re-admission to NHS hospitals within 30 days of discharge. Though the models had a low sensitivity, we show how to identify subgroups of patients that contain a high proportion of patients who will be re-admitted within 30 days. Additional work is necessary to validate the model in practice.
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http://dx.doi.org/10.1136/bmjopen-2012-001667DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3425907PMC
October 2012

Effect of telehealth on use of secondary care and mortality: findings from the Whole System Demonstrator cluster randomised trial.

BMJ 2012 Jun 21;344:e3874. Epub 2012 Jun 21.

The Nuffield Trust, London W1G 7LP, UK.

Objective: To assess the effect of home based telehealth interventions on the use of secondary healthcare and mortality.

Design: Pragmatic, multisite, cluster randomised trial comparing telehealth with usual care, using data from routine administrative datasets. General practice was the unit of randomisation. We allocated practices using a minimisation algorithm, and did analyses by intention to treat.

Setting: 179 general practices in three areas in England.

Participants: 3230 people with diabetes, chronic obstructive pulmonary disease, or heart failure recruited from practices between May 2008 and November 2009.

Interventions: Telehealth involved remote exchange of data between patients and healthcare professionals as part of patients' diagnosis and management. Usual care reflected the range of services available in the trial sites, excluding telehealth.

Main Outcome Measure: Proportion of patients admitted to hospital during 12 month trial period.

Results: Patient characteristics were similar at baseline. Compared with controls, the intervention group had a lower admission proportion within 12 month follow-up (odds ratio 0.82, 95% confidence interval 0.70 to 0.97, P = 0.017). Mortality at 12 months was also lower for intervention patients than for controls (4.6% v 8.3%; odds ratio 0.54, 0.39 to 0.75, P < 0.001). These differences in admissions and mortality remained significant after adjustment. The mean number of emergency admissions per head also differed between groups (crude rates, intervention 0.54 v control 0.68); these changes were significant in unadjusted comparisons (incidence rate ratio 0.81, 0.65 to 1.00, P = 0.046) and after adjusting for a predictive risk score, but not after adjusting for baseline characteristics. Length of hospital stay was shorter for intervention patients than for controls (mean bed days per head 4.87 v 5.68; geometric mean difference -0.64 days, -1.14 to -0.10, P = 0.023, which remained significant after adjustment). Observed differences in other forms of hospital use, including notional costs, were not significant in general. Differences in emergency admissions were greatest at the beginning of the trial, during which we observed a particularly large increase for the control group.

Conclusions: Telehealth is associated with lower mortality and emergency admission rates. The reasons for the short term increases in admissions for the control group are not clear, but the trial recruitment processes could have had an effect.

Trial Registration Number: International Standard Randomised Controlled Trial Number Register ISRCTN43002091.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3381047PMC
http://dx.doi.org/10.1136/bmj.e3874DOI Listing
June 2012

The role of matched controls in building an evidence base for hospital-avoidance schemes: a retrospective evaluation.

Health Serv Res 2012 Aug 6;47(4):1679-98. Epub 2012 Jan 6.

Nuffield Trust, London, United Kingdom.

Objective: To test whether two hospital-avoidance interventions altered rates of hospital use: "intermediate care" and "integrated care teams."

Data Sources/study Setting: Linked administrative data for England covering the period 2004 to 2009.

Study Design: This study was commissioned after the interventions had been in place for several years. We developed a method based on retrospective analysis of person-level data comparing health care use of participants with that of prognostically matched controls.

Data Collection/extraction Methods: Individuals were linked to administrative datasets through a trusted intermediary and a unique patient identifier.

Principal Findings: Participants who received the intermediate care intervention showed higher rates of unscheduled hospital admission than matched controls, whereas recipients of the integrated care team intervention showed no difference. Both intervention groups showed higher rates of mortality than did their matched controls.

Conclusions: These are potentially powerful techniques for assessing impacts on hospital activity. Neither intervention reduced admission rates. Although our analysis of hospital utilization controlled for a wide range of observable characteristics, the difference in mortality rates suggests that some residual confounding is likely. Evaluation is constrained when performed retrospectively, and careful interpretation is needed.
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http://dx.doi.org/10.1111/j.1475-6773.2011.01367.xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3401405PMC
August 2012

A person based formula for allocating commissioning funds to general practices in England: development of a statistical model.

BMJ 2011 Nov 22;343:d6608. Epub 2011 Nov 22.

The Nuffield Trust, London W1G 7LP, UK.

Objectives: To develop a formula for allocating resources for commissioning hospital care to all general practices in England based on the health needs of the people registered in each practice

Design: Multivariate prospective statistical models were developed in which routinely collected electronic information from 2005-6 and 2006-7 on individuals and the areas in which they lived was used to predict their costs of hospital care in the next year, 2007-8. Data on individuals included all diagnoses recorded at any inpatient admission. Models were developed on a random sample of 5 million people and validated on a second random sample of 5 million people and a third sample of 5 million people drawn from a random sample of practices.

Setting: All general practices in England as of 1 April 2007. All NHS inpatient admissions and outpatient attendances for individuals registered with a general practice on that date.

Subjects: All individuals registered with a general practice in England at 1 April 2007.

Main Outcome Measures: Power of the statistical models to predict the costs of the individual patient or each practice's registered population for 2007-8 tested with a range of metrics (R(2) reported here). Comparisons of predicted costs in 2007-8 with actual costs incurred in the same year were calculated by individual and by practice.

Results: Models including person level information (age, sex, and ICD-10 codes diagnostic recorded) and a range of area level information (such as socioeconomic deprivation and supply of health facilities) were most predictive of costs. After accounting for person level variables, area level variables added little explanatory power. The best models for resource allocation could predict upwards of 77% of the variation in costs at practice level, and about 12% at the person level. With these models, the predicted costs of about a third of practices would exceed or undershoot the actual costs by 10% or more. Smaller practices were more likely to be in these groups.

Conclusions: A model was developed that performed well by international standards, and could be used for allocations to practices for commissioning. The best formulas, however, could predict only about 12% of the variation in next year's costs of most inpatient and outpatient NHS care for each individual. Person-based diagnostic data significantly added to the predictive power of the models.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3222692PMC
http://dx.doi.org/10.1136/bmj.d6608DOI Listing
November 2011

Do 'virtual wards' reduce rates of unplanned hospital admissions, and at what cost? A research protocol using propensity matched controls.

Int J Integr Care 2011 Apr 30;11:e079. Epub 2011 Jun 30.

The Nuffield Trust, 59 New Cavendish Street, London W1G 7LP, UK.

Background: This retrospective study will assess the extent to which multidisciplinary case management in the form of virtual wards (VWs) leads to changes in the use of health care and social care by patients at high risk of future unplanned hospital admission. VWs use the staffing, systems and daily routines of a hospital ward to deliver coordinated care to patients in their own homes. Admission to a VW is offered to patients identified by a predictive risk model as being at high risk of unplanned hospital admission in the coming 12 months.

Study Design And Data Collection Methods: We will compare the health care and social care use of VW patients to that of matched controls. Controls will be drawn from (a) national, and (b) local, individual-level pseudonymous routine data. The costs of setting up and running a VW will be determined from the perspectives of both health and social care organizations using a combination of administrative data, interviews and diaries.

Methods Of Analysis: Using propensity score matching and prognostic matching, we will create matched comparator groups to estimate the effect size of virtual wards in reducing unplanned hospital admissions.

Conclusions: THIS STUDY WILL ALLOW US TO DETERMINE RELATIVE TO MATCHED COMPARATOR GROUPS: whether VWs reduce the use of emergency hospital care; the impact, if any, of VWs on the uptake of primary care, community health services and council-funded social care; and the potential costs and savings of VWs from the perspectives of the national health service (NHS) and local authorities.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3178802PMC
http://dx.doi.org/10.5334/ijic.654DOI Listing
April 2011

Predicting who will use intensive social care: case finding tools based on linked health and social care data.

Age Ageing 2011 Mar 20;40(2):265-70. Epub 2011 Jan 20.

The Nuffield Trust, 59 New Cavendish Street, London W1G 7LP, UK.

Background: the costs of delivering health and social care services are rising as the population ages and more people live with chronic diseases.

Objectives: to determine whether predictive risk models can be built that use routine health and social care data to predict which older people will begin receiving intensive social care.

Design: analysis of pseudonymous, person-level, data extracted from the administrative data systems of local health and social care organisations.

Setting: five primary care trust areas in England and their associated councils with social services responsibilities.

Subjects: people aged 75 or older registered continuously with a general practitioner in five selected areas of England (n = 155,905).

Methods: multivariate statistical analysis using a split sample of data.

Results: it was possible to construct models that predicted which people would begin receiving intensive social care in the coming 12 months. The performance of the models was improved by selecting a dependent variable based on a lower cost threshold as one of the definitions of commencing intensive social care.

Conclusions: predictive models can be constructed that use linked, routine health and social care data for case finding in social care settings.
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http://dx.doi.org/10.1093/ageing/afq181DOI Listing
March 2011

Substance use treatment barriers for patients with frequent hospital admissions.

J Subst Abuse Treat 2010 Jan 21;38(1):22-30. Epub 2009 Jun 21.

Department of Emergency Medicine, Division of General Internal Medicine, NYU School of Medicine/Bellevue Hospital Center, New York, NY 10016, USA.

Substance use (SU) disorders adversely impact health status and contribute to inappropriate health services use. This qualitative study sought to determine SU-related factors contributing to repeated hospitalizations and to identify opportunities for preventive interventions. Fifty Medicaid-insured inpatients identified by a validated statistical algorithm as being at high-risk for frequent hospitalizations were interviewed at an urban public hospital. Patient drug/alcohol history, experiences with medical, psychiatric and addiction treatment, and social factors contributing to readmission were evaluated. Three themes related to SU and frequent hospitalizations emerged: (a) barriers during hospitalization to planning long-term treatment and follow-up, (b) use of the hospital as a temporary solution to housing/family problems, and (c) unsuccessful SU aftercare following discharge. These data indicate that homelessness, brief lengths of stay complicating discharge planning, patient ambivalence regarding long-term treatment, and inadequate detox-to-rehab transfer resources compromise substance-using patients' likelihood of avoiding repeat hospitalization. Intervention targets included supportive housing, detox-to-rehab transportation, and postdischarge patient support.
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http://dx.doi.org/10.1016/j.jsat.2009.05.009DOI Listing
January 2010

Medicaid patients at high risk for frequent hospital admission: real-time identification and remediable risks.

J Urban Health 2009 Mar 12;86(2):230-41. Epub 2008 Dec 12.

Department of Emergency Medicine, NYU School of Medicine, New York, NY 10016, USA.

Patients with frequent hospitalizations generate a disproportionate share of hospital visits and costs. Accurate determination of patients who might benefit from interventions is challenging: most patients with frequent admissions in 1 year would not continue to have them in the next. Our objective was to employ a validated regression algorithm to case-find Medicaid patients at high-risk for hospitalization in the next 12 months and identify intervention-amenable characteristics to reduce hospitalization risk. We obtained encounter data for 36,457 Medicaid patients with any visit to an urban public hospital from 2001 to 2006 and generated an algorithm-based score for hospitalization risk in the subsequent 12 months for each patient (0 = lowest, 100 = highest). To determine medical and social contributors to the current admission, we conducted in-depth interviews with high-risk hospitalized patients (scores >50) and analyzed associated Medicaid claims data. An algorithm-based risk score >50 was attained in 2,618 (7.2%) patients. The algorithm's positive predictive value was equal to 0.67. During the study period, 139 high-risk patients were admitted: 60 met inclusion criteria and 50 were interviewed. Fifty-six percent cited the Emergency Department as their usual source of care or had none. Sixty-eight percent had >1 chronic medical conditions, and 42% were admitted for conditions related to substance use. Sixty percent were homeless or precariously housed. Mean Medicaid expenditures for the interviewed patients were $39,188 and $84,040 per patient for the years immediately prior to and following study participation, respectively. Findings including high rates of substance use, homelessness, social isolation, and lack of a medical home will inform the design of interventions to improve community-based care and reduce hospitalizations and associated costs.
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http://dx.doi.org/10.1007/s11524-008-9336-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2648879PMC
March 2009

Some reflections on a few of the pitfalls in the world of foundation grant making.

Authors:
John Billings

Health Aff (Millwood) 2007 Nov-Dec;26(6):1772-5

New York University's Robert F. Wagner Graduate School of Public Service, in New York City, USA.

This paper offers some reflections on the grant-making process from a former foundation executive. Some of the opportunities, challenges, and pitfalls inherent in the foundation world are described, and one approach to grant making, the "call for proposals," is examined as an example of the need for greater attention to and investment in the science of grant making itself, to maximize the potential return from philanthropy.
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http://dx.doi.org/10.1377/hlthaff.26.6.1772DOI Listing
February 2008

Improving the management of care for high-cost Medicaid patients.

Health Aff (Millwood) 2007 Nov-Dec;26(6):1643-54

New York University's Robert F. Wagner Graduate School of Public Service, USA.

Increased policy attention is being focused on the management of high-cost cases in Medicaid. In this paper we present an algorithm that identifies patients at high risk of future hospitalizations and offer a business-case analysis with a range of assumptions about the rate of reduction in future hospitalization and the cost of the intervention. The characteristics of the patients identified by the algorithm are described, and the implications of these findings for policymakers, payers, and providers interested in responding more effectively to the needs of these patients are discussed, including the challenges likely to be encountered in implementing an intervention initiative.
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http://dx.doi.org/10.1377/hlthaff.26.6.1643DOI Listing
February 2008

Notes from the field: jumpstarting the IRB approval process in multicenter studies.

Health Serv Res 2007 Aug;42(4):1773-82

Wagner Graduate School, New York University, Puck Building, 295 Lafayette Street, New York, NY 10012, USA.

Objective: To identify strategies that facilitate readiness for local Institutional Review Board (IRB) review, in multicenter studies.

Study Setting: Eleven acute care hospitals, as they applied to participate in a foundation-sponsored quality improvement collaborative.

Study Design: Case series.

Data Collection/extraction: Participant observation, supplemented with review of written and oral communications.

Principal Findings: Applicant hospitals responded positively to efforts to engage them in early planning for the IRB review process. Strategies that were particularly effective were the provisions of application templates, a modular approach to study description, and reliance on conference calls to collectively engage prospective investigators, local IRB members, and the evaluation/national program office teams. Together, these strategies allowed early identification of problems, clarification of intent, and relatively timely completion of the local IRB review process, once hospitals were selected to participate in the learning collaborative.

Conclusions: Engaging potential collaborators in planning for IRB review may help expedite and facilitate review, without compromising the fairness of the grant-making process or the integrity of human subjects protection.
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http://dx.doi.org/10.1111/j.1475-6773.2006.00687.xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1955279PMC
August 2007

Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients.

BMJ 2006 Aug 30;333(7563):327. Epub 2006 Jun 30.

Centre for Health and Public Service Research, New York University, 295 Lafayette St, New York, NY 10012, USA.

Objective: To develop a method of identifying patients at high risk of readmission to hospital in the next 12 months for practical use by primary care trusts and general practices in the NHS in England.

Data Sources: Data from hospital episode statistics showing all admissions in NHS trusts in England over five years, 1999-2000 to 2003-4; data from the 2001 census for England. Population All residents in England admitted to hospital in the previous four years with a subset of "reference" conditions for which improved management may help to prevent future admissions.

Design: Multivariate statistical analysis of routinely collected data to develop an algorithm to predict patients at highest risk of readmission in the next 12 months. The algorithm was developed by using a 10% sample of hospital episode statistics data for all of England for the period indicated. The coefficients for 21 most powerful (and statistically significant) variables were then applied against a second 10% test sample to validate the findings of the algorithm from the first sample.

Results: The key factors predicting subsequent admission included age, sex, ethnicity, number of previous admissions, and clinical condition. The algorithm produces a risk score (from 0 to 100) for each patient admitted with a reference condition. At a risk score threshold of 50, the algorithm identified 54.3% of patients admitted with a reference condition who would have an admission in the next 12 months; 34.7% of patients were "flagged" incorrectly (they would not have a subsequent admission). At risk score threshold levels of 70 and 80, the rate of incorrectly "flagged" patients dropped to 22.6% and 15.7%, but the algorithm found a lower percentage of patients who would be readmitted. The algorithm is made freely available to primary care trusts via a website.

Conclusions: A method of predicting individual patients at highest risk of readmission to hospital in the next 12 months has been developed, which has a reasonable level of sensitivity and specificity. Using various assumptions a "business case" has been modelled to demonstrate to primary care trusts and practices the potential costs and impact of an intervention using the algorithm to reduce hospital admissions.
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http://dx.doi.org/10.1136/bmj.38870.657917.AEDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1539047PMC
August 2006

The role of faith-based institutions in addressing health disparities: a case study of an initiative in the southwest Bronx.

J Health Care Poor Underserved 2006 May;17(2 Suppl):9-19

Robert F. Wagner Graduate School of Public Service, New York University, USA.

Although many public health initiatives have been implemented through collaborations with faith-based institutions, little is known about best practices for developing such programs. Using a community-based participatory approach, this case study examines the implementation of an initiative in the Bronx, New York, that is designed to educate community members about health promotion and disease management and to mobilize church members to seek equal access to health care services. The study used qualitative methods, including the collaborative development of a logic model for the initiative, focus groups, interviews, analysis of program reports, and participant observation. The paper examines three key aspects of the initiative's implementation: (1) the engagement of the church leadership; (2) the use of church structures as venues for education and intervention; and (3) changes in church policies. Key findings include the importance of pre-existing relationships within the community and the prominent agenda-setting role played by key pastors, and the strength of the Coalition's dual focus on health behaviors and health disparities. Given the churches' demonstrated ability to pull people together, to motivate and to inspire, there is great potential for faith-based interventions, and models developed through such interventions, to address health disparities.
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http://dx.doi.org/10.1353/hpu.2006.0088DOI Listing
May 2006
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