Telemedicine, the use of audiovisual technology to provide health care from a remote location, is increasingly used in intensive care units (ICUs). However, studies evaluating the impact of ICU telemedicine show mixed results, with some studies demonstrating improved patient outcomes, while others show limited benefit or even harm. Little is known about the mechanisms that influence variation in ICU telemedicine effectiveness, leaving providers without guidance on how to best use this potentially transformative technology. The Contributors to Effective Critical Care Telemedicine (ConnECCT) study aims to fill this knowledge gap by identifying the clinical and organizational factors associated with variation in ICU telemedicine effectiveness, as well as exploring the clinical contexts and provider perceptions of ICU telemedicine use and its impact on patient outcomes, using a range of qualitative methods. In this report, we describe the study protocol, data collection methods, and planned future analyses of the ConnECCT study. Over the course of 1 year, the study team visited purposefully sampled health systems across the United States that have adopted telemedicine. Data collection methods included direct observations, interviews, focus groups, and artifact collection. Data were collected at the ICUs that provide in-person critical care as well as at the supporting telemedicine units. Iterative thematic content analysis will be used to identify and define key constructs related to telemedicine effectiveness and describe the relationship between them. Ultimately, the study results will provide a framework for more effective implementation of ICU telemedicine, leading to improved clinical outcomes for critically ill patients.
Publications
2017
BACKGROUND: In October 2015, the Centers for Medicare and Medicaid Services (CMS) implemented the Sepsis CMS Core Measure (SEP-1) program, requiring hospitals to report data on the quality of care for their patients with sepsis.
OBJECTIVE: We sought to understand hospital perceptions of and responses to the SEP-1 program.
DESIGN: A thematic content analysis of semistructured interviews with hospital quality officials.
SETTING: A stratified random sample of short-stay, nonfederal, general acute care hospitals in the United States.
PATIENTS: Hospital quality officers, including nurses and physicians.
MEASUREMENTS: We completed 29 interviews before reaching content saturation.
RESULTS: Hospitals reported a variety of actions in response to SEP-1, including new efforts to collect data, improve sepsis diagnosis and treatment, and manage clinicians' attitudes toward SEP-1. These efforts frequently required dedicated resources to meet the program's requirements for treatment and documentation, which were thought to be complex and not consistently linked to patient-centered outcomes. Most respondents felt that SEP-1 was likely to improve sepsis outcomes. At the same time, they described specific changes that could improve its effectiveness, including allowing hospitals to focus on the treatment processes most directly associated with improved patient outcomes and better aligning the measure's sepsis definitions with current clinical definitions.
CONCLUSIONS: Hospitals are responding to the SEP-1 program across a number of domains and in ways that consistently require dedicated resources. Hospitals are interested in further revisions to the program to alleviate the burden of the reporting requirements and help them optimize the effectiveness of their investments in quality-improvement efforts.
OBJECTIVE: Although there is growing evidence regarding the utility of telemedicine in providing care for acutely ill children in underserved settings, adoption of pediatric emergency telemedicine remains limited, and little data exist to inform implementation efforts. Among clinician stakeholders, we examined attitudes regarding pediatric emergency telemedicine, including barriers to adoption in rural settings and potential strategies to overcome these barriers.
METHODS: Using a sequential mixed-methods approach, we first performed semistructured interviews with clinician stakeholders using thematic content analysis to generate a conceptual model for pediatric emergency telemedicine adoption. Based on this model, we then developed and fielded a survey to further examine attitudes regarding barriers to adoption and strategies to improve adoption.
RESULTS: Factors influencing adoption of pediatric emergency telemedicine were identified and categorized into 3 domains: contextual factors (such as regional geography, hospital culture, and individual experience), perceived usefulness of pediatric emergency telemedicine, and perceived ease of use of pediatric emergency telemedicine. Within the domains of perceived usefulness and perceived ease of use, belief in the relative advantage of telemedicine was the most pronounced difference between telemedicine proponents and nonproponents. Strategies identified to improve adoption of telemedicine included patient-specific education, clinical protocols for use, decreasing response times, and simplifying the technology.
CONCLUSIONS: More effective adoption of pediatric emergency telemedicine among clinicians will require addressing perceived usefulness and perceived ease of use in the context of local factors. Future studies should examine the impact of specific identified strategies on adoption of pediatric emergency telemedicine and patient outcomes in rural settings.
BACKGROUND: Veterans commonly receive care from both Veterans Health Administration (VA) and non-VA sources (i.e., dual use). A major challenge in comparing health outcomes between dual users and VA-predominant users is applying an accurate method of risk adjustment.
OBJECTIVE: To determine how different comorbidity indices affect the association between patterns of dual use and health outcomes.
DESIGN: Retrospective cohort.
PARTICIPANTS: A total of 316,775 community-dwelling Veterans (≥65 years) with type 2 diabetes who were enrolled in VA and fee-for-service Medicare from 2008 to 2010.
METHODS: We determined the associations between dual use and death or diabetes-related hospitalization in FY 2010 using multivariable models incorporating claims-based (Elixhauser) or medication-based (RxRisk-V) risk adjustment. Dual use was classified using four previously identified groups of health services users: 1) VA-predominant, 2) VA + Medicare visits and labs, 3) VA + Medicare test strips, and 4) VA + Medicare medications.
KEY RESULTS: Controlling for Elixhauser comorbidities, dual-use groups 2-4 had significantly decreased odds of death or hospitalization compared to VA-predominant users. Controlling for RxRisk-V comorbidities, groups 2-4 had increased odds of death compared to VA-predominant users, but variable odds of hospitalization, with group 2 having increased odds (OR 1.06, CI 1.04-1.09), while groups 3 (OR 0.96, CI 0.94-0.99) and 4 (OR 0.93, CI 0.89-0.97) had decreased odds.
CONCLUSIONS: The method of risk adjustment drastically influences the direction of effect in health outcomes among dual users of VA and Medicare. These findings underscore the need for standardized and reliable risk adjustment methods that are not susceptible to measurement differences across different health systems.
OBJECTIVE: To assess changes in cervical cancer screening after the 2009 American College of Obstetricians and Gynecologists' guideline change and to determine predictors associated with underscreening and overscreening among Medicaid-enrolled women.
METHODS: We performed an observational cohort study of Pennsylvania Medicaid claims from 2007 to 2013. We evaluated guideline adherence of 18- to 64-year-old continuously enrolled women before and after the 2009 guideline change. To define adherence, we categorized intervals between Pap tests as longer than (underscreening), within (appropriate screening), or shorter than (overscreening) guideline-recommended intervals (±6-month). We stratified results by age and assessed predictors of underscreening and overscreening through logistic regression.
RESULTS: Among 29,650 women, appropriate cervical cancer screening significantly decreased after the guideline change (from 45% [95% confidence interval (CI) 44-46%] to 11% [95% CI 11-12%] among 17,360 younger than 30 year olds and from 27% [95% CI 26-28%] to 6% [95% CI 6-7%] among 12,290 women 30 years old or older). Overscreening significantly increased (from 6% [95% CI 5-6%] to 67% [95% CI 66-68%] in those younger than 30 years old and from 54% [95% CI 52-55%] to 65% [95% CI 64-67%] in those 30 years old or older), whereas underscreening significantly increased only in those 30 years old or older (from 20% [95% CI 19-21%] to 29% [95% CI 27-30%]). Pap tests after guideline change, pregnancy, Managed Care enrollment (in those younger than 30 years old), and black race (in those younger than 30 years old) were associated with underscreening. Pap tests after guideline change, more visits, more sexually transmitted infection testing, and white race (in those 30 years old or older) were associated with overscreening.
CONCLUSION: We observed high rates of cervical cancer overscreening and underscreening and low rates of appropriate screening after the guideline change. Interventions should target both underscreening and overscreening to address these separate yet significant issues.
BACKGROUND AND AIMS: Global payment and accountable care reform efforts in the United States may connect more individuals with substance use disorders (SUD) to treatment. We tested whether such changes instituted under an Alternative Quality Contract (AQC) model within the Blue Cross Blue Shield of Massachusetts' (BCBSMA) insurer increased care for individuals with SUD.
DESIGN: Difference-in-differences design comparing enrollees in AQC organizations with a comparison group of enrollees in organizations not participating in the AQC.
SETTING: Massachusetts, USA.
PARTICIPANTS: BCBSMA enrollees aged 13-64 years from 2006 to 2011 (3 years prior to and after implementation) representing 1 333 534 enrollees and 42 801 SUD service users.
MEASUREMENTS: Outcomes were SUD service use and spending and SUD performance metrics. Primary exposures were enrollment into an AQC provider organization and whether the AQC organization did or did not face risk for behavioral health costs.
FINDINGS: Enrollees in AQC organizations facing behavioral health risk experienced no change in the probability of using SUD services (1.64 versus 1.66%; P = 0.63), SUD spending ($2807 versus $2700; P = 0.34) or total spending ($12 631 versus $12 849; P = 0.53), or SUD performance metrics (identification: 1.73 versus 1.76%, P = 0.57; initiation: 27.86 versus 27.02%, P = 0.50; engagement: 11.19 versus 10.97%, P = 0.79). Enrollees in AQC organizations not at risk for behavioral health spending experienced a small increase in the probability of using SUD services (1.83 versus 1.66%; P = 0.003) and the identification performance metric (1.92 versus 1.76%; P = 0.007) and a reduction in SUD medication use (11.84 versus 14.03%; P = 0.03) and the initiation performance metric (23.76 versus 27.02%; P = 0.005).
CONCLUSIONS: A global payment and accountable care model introduced in Massachusetts, USA (in which a health insurer provided care providers with fixed prepayments to cover most or all of their patients' care during a specified time-period, incentivizing providers to keep their patients healthy and reduce costs) did not lead to sizable changes in substance use disorder service use during the first 3 years following its implementation.
OBJECTIVES: The US opioid medication epidemic has resulted in serious health consequences for patients. Formulary management tools adopted by payers, specifically prior authorization (PA) policies, may lower the rates of opioid medication abuse and overdose. We compared rates of opioid abuse and overdose among enrollees in plans that varied in their use of PA from "High PA" (ie, required PA for 17 to 74 opioids), with "Low PA" (ie, required PA for 1 opioid), and "No PA" policies for opioid medications.
STUDY DESIGN: Retrospective cohort study of patients initiating opioid treatment in Pennsylvania Medicaid from 2010 to 2012.
METHODS: Generalized linear models with generalized estimating equations were employed to assess the relationships between the presence of PA policies and opioid medication abuse and overdose, as measured in Medicaid claims data, adjusting for demographics, comorbid health conditions, benzodiazepine/muscle relaxant use, and emergency department use.
RESULTS: The study cohort included 297,634 enrollees with a total of 382,828 opioid treatment episodes. Compared with plans with No PA, enrollees in High PA (adjusted rate ratio [ARR], 0.89; 95% confidence interval [CI], 0.85-0.93; P <.001) and Low PA plans (ARR, 0.93; 95% CI, 0.87-1.00; P = .04) had lower rates of abuse. Enrollees in the Low PA plan had a lower rate of overdose than those within plans with No PA (ARR, 0.75; 95% CI, 0.59-0.95; P = .02). High PA plan enrollees were also less likely than No PA enrollees to experience an overdose, but this association was not statistically significant (ARR, 0.88; 95% CI, 0.76-1.02; P = .08).
CONCLUSIONS: Enrollees within Medicaid plans that utilize PA policies appear to have lower rates of abuse and overdose following initiation of opioid medication treatment.
WHAT IS KNOWN AND OBJECTIVE: There are few studies examining both drug-drug and drug-disease interactions in older adults. Therefore, the objective of this study was to describe the prevalence of potential drug-drug and drug-disease interactions and associated factors in community-dwelling older adults.
METHODS: This cross-sectional study included 3055 adults aged 70-79 without mobility limitations at their baseline visit in the Health Aging and Body Composition Study conducted in the communities of Pittsburgh PA and Memphis TN, USA. The outcome factors were potential drug-drug and drug-disease interactions as per the application of explicit criteria drawn from a number of sources to self-reported prescription and non-prescription medication use.
RESULTS: Over one-third of participants had at least one type of interaction. Approximately one quarter (25·1%) had evidence of had one or more drug-drug interactions. Nearly 10·7% of the participants had a drug-drug interaction that involved a non-prescription medication. % The most common drug-drug interaction was non-steroidal anti-inflammatory drugs (NSAIDs) affecting antihypertensives. Additionally, 16·0% had a potential drug-disease interaction with 3·7% participants having one involving non-prescription medications. The most common drug-disease interaction was aspirin/NSAID use in those with history of peptic ulcer disease without gastroprotection. Over one-third (34·0%) had at least one type of drug interaction. Each prescription medication increased the odds of having at least one type of drug interaction by 35-40% [drug-drug interaction adjusted odds ratio (AOR) = 1·35, 95% confidence interval (CI) = 1·27-1·42; drug-disease interaction AOR = 1·30; CI = 1·21-1·40; and both AOR = 1·45; CI = 1·34-1·57]. A prior hospitalization increased the odds of having at least one type of drug interaction by 49-84% compared with those not hospitalized (drug-drug interaction AOR = 1·49, 95% CI = 1·11-2·01; drug-disease interaction AOR = 1·69, CI = 1·15-2·49; and both AOR = 1·84, CI = 1·20-2·84).
WHAT IS NEW AND CONCLUSION: Drug interactions are common among community-dwelling older adults and are associated with the number of medications and hospitalization in the previous year. Longitudinal studies are needed to evaluate the impact of drug interactions on health-related outcomes.
BACKGROUND: Health systems may play an important role in identification of patients at-risk of opioid medication overdose. However, standard measures for identifying overdose risk in administrative data do not exist.
OBJECTIVE: Examine the association between opioid medication overdose and 2 validated measures of nonmedical use of prescription opioids within claims data.
RESEARCH DESIGN: A longitudinal retrospective cohort study that estimated associations between overdose and nonmedical use.
SUBJECTS: Adult Pennsylvania Medicaid program 2007-2012 patients initiating opioid treatment who were: nondual eligible, without cancer diagnosis, and not in long-term care facilities or receiving hospice.
MEASURES: Overdose (International Classification of Disease, ninth edition, prescription opioid poisonings codes), opioid abuse (opioid use disorder diagnosis while possessing an opioid prescription), opioid misuse (a composite indicator of number of opioid prescribers, number of pharmacies, and days supplied), and dose exposure during opioid treatment episodes.
RESULTS: A total of 372,347 Medicaid enrollees with 583,013 new opioid treatment episodes were included in the cohort. Opioid overdose was higher among those with abuse (1.5%) compared with those without (0.2%, P<0.001). Overdose was higher among those with probable (1.8%) and possible (0.9%) misuse compared with those without (0.2%, P<0.001). Abuse [adjusted rate ratio (ARR), 1.52; 95% confidence interval (CI), 1.10-2.10), probable misuse (ARR, 1.98; 95% CI, 1.46-2.67), and possible misuse (ARR, 1.76; 95% CI, 1.48-2.09) were associated with significantly more events of opioid medication overdose compared with those without.
CONCLUSIONS: Claims-based measures can be used by health systems to identify individuals at-risk of overdose who can be targeted for restrictions on opioid prescribing, dispensing, or referral to treatment.