AI, working with standard CT scans, could help predict under-diagnosed conditions

Israeli healthcare system Calit Health System teamed with analytics company Zebra Medical Vision to develop ways artificial intelligence could be leveraged to help predict two major under-diagnosed medical conditions: osteoporotic fractures and coronary vascular events. Using CT scan data, the AI tools showed improvements in predicting instances of both.

In each case, when AI was paired with existing predictive and diagnostic measures it was able to improve both the "sensitivity and specificity" of the clinical predictions, leading to much better predictions of future health events, said Calit Health and Zebra Medical officials.

The full Healthcare IT News article can be viewed at this link.  

Precision immunoprofiling by image analysis and artificial intelligence

Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This requirement can be met by a combination of computational pathology and artificial intelligence. Here, we critically assess computational approaches supporting the development of a standardized methodology in the assessment of immune-oncology biomarkers, such as PD-L1 and immune cell infiltrates. We examine immunoprofiling through spatial analysis of tumor-immune cell interactions and multiplexing technologies as a predictor of patient response to cancer treatment. Further, we discuss how integrated bioinformatics can enable the amalgamation of complex morphological phenotypes with the multiomics datasets that drive precision medicine. We provide an outline to machine learning (ML) and artificial intelligence tools and illustrate fields of application in immune-oncology, such as pattern-recognition in large and complex datasets and deep learning approaches for survival analysis. Synergies of surgical pathology and computational analyses are expected to improve patient stratification in immuno-oncology. We propose that future clinical demands will be best met by (1) dedicated research at the interface of pathology and bioinformatics, supported by professional societies, and (2) the integration of data sciences and digital image analysis in the professional education of pathologists.

The full article can be downloaded below.  

Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse

Enormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems.

In this paper, we discuss whether data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of data.

This paper is based on the authors’ experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records.

We first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature.

There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven health care will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.

The full article can be downloaded below.  

Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff

AI systems are being deployed to support human decision making in high-stakes domains such as healthcare and criminal justice. In many cases, the human and AI form a team, in which the human makes decisions after reviewing the AI’s inferences. A successful partnership requires that the human develops insights into the performance of the AI system, including its failures. We study the influence of updates to an AI system in this setting. While updates can increase the AI’s predictive performance, they may also lead to behavioral changes that are at odds with the user’s prior experiences and confidence in the AI’s inferences. We show that updates that increase AI performance may actually hurt team performance. We introduce the notion of the compatibility of an AI update with prior user experience and present methods for studying the role of compatibility in human-AI teams. Empirical results on three high-stakes classification tasks show that current machine learning algorithms do not produce compatible updates. We propose a re-training objective to improve the compatibility of an update by penalizing new errors. The objective offers full leverage of the performance/compatibility tradeoff across different datasets, enabling more compatible yet accurate updates.

The full article can be downloaded below.  

Data Analytics and Modeling for Appointment No-show in Community Health Centers

Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models’ ability to identify patients missing their appointments. Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier). Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues. EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions.

The full article can be downloaded below.  

Machine learning approaches for predicting high cost high need patient expenditures in health care

This paper studies the temporal consistency of health care expenditures in a large state Medicaid program. Predictive machine learning models were used to forecast the expenditures, especially for the high-cost, high-need (HCHN) patients. We systematically tests temporal correlation of patient-level health care expenditures in both the short and long terms. The results suggest that medical expenditures are significantly correlated over multiple periods. Our work demonstrates a prevalent and strong temporal correlation and shows promise for predicting future health care expenditures using machine learning. Temporal correlation is stronger in HCHN patients and their expenditures can be better predicted. Including more past periods is beneficial for better predictive performance. This study shows that there is significant temporal correlation in health care expenditures. Machine learning models can help to accurately forecast the expenditures. These results could advance the field toward precise preventive care to lower overall health care costs and deliver care more efficiently.

The full article can be downloaded below.  

Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records

Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Machine learning methods are capable of capturing complex interactions that are likely to be present when predicting less specific outcomes, such as this one.

The use of machine learning and addition of temporal information led to substantially improved discrimination and calibration for predicting the risk of emergency admission. Model performance remained stable across a range of prediction time windows and when externally validated. These findings support the potential of incorporating machine learning models into electronic health records to inform care and service planning.

The full article can be downloaded below.  

Healthy volunteers’ perceptions of risk in US Phase I clinical trials: A mixed-methods study

There is limited research on healthy volunteers’ perceptions of the risks of Phase I clinical trials. In order to contribute empirically to long-standing ethical concerns about healthy volunteers’ involvement in drug development, it is crucial to assess how these participants understand trial risks. The objectives of this study were to investigate (1) participants’ views of the overall risks of Phase I trials, (2) their views of the risk of personally being harmed in a trial, and (3) how risk perceptions vary across participants’ clinical trial history and sociodemographic characteristics.

We qualitatively and quantitatively analyzed semi-structured interviews conducted with 178 healthy volunteers who had participated in a diverse range of Phase I trials in the United States. Participants had collective experience in a reported 1,948 Phase I trials (mean = 10.9; median = 5), and they were interviewed as part of a longitudinal study of healthy volunteers’ risk perceptions, their trial enrollment decisions, and their routine health behaviors. Participants’ qualitative responses were coded, analyzed, and subsequently quantified in order to assess correlations between their risk perceptions and demographics, such as their race/ethnicity, gender, age, educational attainment, employment status, and household income. We found that healthy volunteers often viewed the overall risks of Phase I trials differently than their own personal risk of harm. The majority of our participants thought that Phase I trials were medium, high, or extremely high risk (118 of 178), but most nonetheless felt that they were personally safe from harm (97 of 178). We also found that healthy volunteers in their first year of clinical trial participation, racial and ethnic minority participants, and Hispanic participants tended to view the overall trial risks as high (respectively, Jonckheere-Terpstra, −2.433, p = 0.015; Fisher exact test, p = 0.016; Fisher exact test, p = 0.008), but these groups did not differ in regard to their perceptions of personal risk of harm (respectively, chi-squared, 3.578, p = 0.059; chi-squared, 0.845, p = 0.358; chi-squared, 1.667, p =0.197). The main limitation of our study comes from quantitatively aggregating data from indepth interviews, which required the research team to interpret participants’ nonstandardized risk narratives.

Our study demonstrates that healthy volunteers are generally aware of and reflective about Phase I trial risks. The discrepancy in healthy volunteers’ views of overall and personal risk sheds light on why healthy volunteers might continue to enroll in clinical trials, even when they view trials on the whole as risky.

The full article can be downloaded below.  

From Point-of-Care Testing to eHealth Diagnostic Devices (eDiagnostics)

Point-of-care devices were originally designed to allow medical testing at or near the point of care by healthcare professionals. Some point-of-care devices allow medical self-testing at home but cannot fully cover the growing diagnostic needs of eHealth systems that are under development in many countries. A number of easy-to-use, network connected diagnostic devices for self-testing are needed to allow remote monitoring of patients’ health. This Outlook highlights the essential characteristics of diagnostic devices for eHealth settings and indicates point-of-care technologies that may lead to the development of new devices. It also describes the most representative examples of simple-to-use, point-of-care devices that have been used for analysis of untreated biological samples.

The full article can be downloaded below.  

Building More Trust Between Doctors and Patients

The general lesson that we have learned from these experiences is that building an empathetic, trust-based relationship with patients is not a nice-to-have but a must-have.  It creates the possibility of identifying underlying hidden conditions whose treatment prevents the occurrence of overt symptomatic conditions that cause distress to patients and place huge strains on the capacity of healthcare services.  Empathy saves not only lives but also money and time.  It’s time to build a place for it in the clinical process.

The full Harvard Business Review article can be viewed at this link.