A pragmatic methodology for the evaluation of digital care management in the context of multimorbidity.

Multimorbidity is a defining challenge for health systems and requires coordination of care delivery and care management. Care management is a clinical service designed to remotely engage patients between visits and after discharge in order to support self-management of chronic and emergent conditions, encourage increased use of scheduled care and address the use of unscheduled care. Care management can be provided using digital technology - digital care management. A robust methodology to assess digital care management, or any traditional or digital primary care intervention aimed at longitudinal management of multimorbidity, does not exist outside of randomized controlled trials (RCTs). RCTs are not always generalizable and are also not feasible for most healthcare organizations. We describe here a novel and pragmatic methodology for the evaluation of digital care management that is generalizable to any longitudinal intervention for multimorbidity irrespective of its mode of delivery. This methodology implements propensity matching with bootstrapping to address some of the major challenges in evaluation including identification of robust outcome measures, selection of an appropriate control population, small sample sizes with class imbalances, and limitations of RCTs. We apply this methodology to the evaluation of digital care management at a U.S. payor and demonstrate a 9% reduction in ER utilization, a 17% reduction in inpatient admissions, and a 29% increase in the utilization of preventive medicine services. From these utilization outcomes, we drive forward an estimated cost saving that is specific to a single payor's payment structure for the study time period of $641 per-member-per-month at 3 months. We compare these results to those derived from existing observational approaches, 1:1 and 1:n propensity matching, and discuss the circumstances in which our methodology has advantages over existing techniques. Whilst our methodology focuses on cost and utilization and is applied in the U.S. context, it is applicable to other outcomes such as Patient Reported Outcome Measures (PROMS) or clinical biometrics and can be used in other health system contexts where the challenge of multimorbidity is prevalent.

A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods.

Digital care management programs can reduce health care costs and improve quality of care. However, it is unclear how to target patients who are most likely to benefit from these programs ex ante, a shortcoming of current "risk score"-based approaches across many interventions. This study explores a framework to define impactability by using machine learning (ML) models to identify those patients most likely to benefit from a digital health intervention for care management. Anonymized insurance claims data were used from a commercially insured population across several US states and combined with inferred sociodemographic data. The approach involves creating 2 models and the comparative analysis of the methodologies and performances therein. The authors first train a cost prediction model to calculate the differences in predicted (without intervention) versus actual (with onboarding onto digital health platform) health care expenditures for patients (N = 5600). This enables classification impactability if differences in predicted versus actual costs meet a predetermined threshold. Several random forest and logistic regression machine learning models were then trained to accurately categorize new patients as impactable versus not impactable. These parameters are modified through grid search to define the parameters that deliver optimal performance, reaching an overall sensitivity of 0.77 and specificity of 0.65 among all models. This approach shows that impactability for a digital health intervention can be successfully defined using ML methods, thus enabling efficient allocation of resources. This framework is generalizable to analyzing impactability of any intervention and can contribute to realizing closed-loop feedback systems for continuous improvement in health care.

Tuning of ferromagnetic spin interactions in polymeric aromatic amines via modification of their π-conjugated system.

Polyarylamine containing meta-para-para-aniline units in the main chain and meta-para-aniline units in the pendant chains was synthesized. The polymer can be oxidized to radical cations in chemical or electrochemical ways. The presence of meta-phenylenes in the polymer chemical structure allows for the ferromagnetic coupling of electronic spins, which leads to the formation of high spin states. Detailed pulsed-EPR study indicates that the S = 2 spin state was reached for the best oxidation level. Quantitative magnetization measurements reveal that the doped polymer contains mainly S = 2 spin states and a fraction of S = 3/2 spin states. The efficiency of the oxidation was determined to be 74%. To the best of our knowledge, this polymer is the first example of a linear doped polyarylamine combining such high spin states with high doping efficiency.

Magnetic properties of a doped linear polyarylamine bearing a high concentration of coupled spins (S = 1).

Magnetic properties of a doped linear polyarylamine (PA2), whose chain includes alternating para-phenylene and meta-phenylene groups, and of two cyclic and linear model compounds (C2 and D2) were explored by pulsed-EPR nutation spectroscopy, SQUID magnetometry and DFT calculations. Stoichiometrically doped PA2 samples exhibit a pure S = 1 state (exchange coupling constant J = 18 K) with a high spin concentration (0.65) corresponding to 65% of mers bearing holes. Such properties were already observed for doped reticulated polyarylamines but are quite unusual for doped linear polyarylamines. In order to better understand the properties of PA2, model compounds C2 and D2 were also investigated: pure S = 1 spin states could also be obtained, but with higher J (respectively 57 K and 35 K) and, surprisingly, with high but still limited spin concentrations (respectively 0.77 and 0.65).