We are now on the cusp of massive adoption of digital health technologies. Medicine is becoming an information science intertwined with technology and data science. This talk aims to describe the current state of digital transformation in healthcare, to identify reasons for enthusiasm and caution, and to provide a framework for thinking about what is necessary for hospitals and health systems to be confident about incorporating these innovations into practice. I have three key recommendations. First, we should buy results, not claims. Those in positions that influence decisions about endorsing or purchasing digital products designed to improve care or outcomes ought to buy results, not claims or intermediate results. Moreover, although analytic validity and clinical validity are important, they sometimes do not reflect the impact of a product in its entirety. Ultimately, we need to know whether patients benefit. Second, we should insist on transparency. The performance of a product cannot be a secret. The basis on which developers make claims about their products should be open to all, including patients. Better yet, data on which experts reach a conclusion should be shared, just as many companies share research data on drugs and devices. Third, we should be aware of unintended adverse consequences. We should evaluate every intervention for unintended adverse consequences. Changes to systems, with all good intentions, can always go awry. In conclusion, insistence on good and evolving evidence is the best way to arrive at our destination: the use of innovations to improve outcomes.

BACKGROUNDDespite the rapid growth in the incidence of acute myocardial infarction (AMI) in China, there is limited information about patients' experiences after AMI hospitalization, especially on long-term adverse events and patient-reported outcomes (PROs).

METHODSThe China Patient-centered Evaluative Assessment of Cardiac Events (PEACE)-Prospective AMI Study will enroll 4000 consecutive AMI patients from 53 diverse hospitals across China and follow them longitudinally for 12 months to document their treatment, recovery, and outcomes. Details of patients' medical history, treatment, and in-hospital outcomes are abstracted from medical charts. Comprehensive baseline interviews are being conducted to characterize patient demographics, risk factors, presentation, and healthcare utilization. As part of these interviews, validated instruments are administered to measure PROs, including quality of life, symptoms, mood, cognition, and sexual activity. Follow-up interviews, measuring PROs, medication adherence, risk factor control, and collecting hospitalization events are conducted at 1, 6, and 12 months after discharge. Supporting documents for potential outcomes are collected for adjudication by clinicians at the National Coordinating Center. Blood and urine samples are also obtained at baseline, 1- and 12-month follow-up. In addition, we are conducting a survey of participating hospitals to characterize their organizational characteristics.

CONCLUSIONThe China PEACE-Prospective AMI study will be uniquely positioned to generate new information regarding patient's experiences and outcomes after AMI in China and serve as a foundation for quality improvement activities.

Acute Disease ; Adult ; China ; Female ; Hospitalization ; Humans ; Male ; Myocardial Infarction ; diagnosis ; Patient-Centered Care ; Prospective Studies ; Quality of Life ; Risk Factors ; Young Adult

BACKGROUNDHyperglycemia on admission has been found to elevate risk for mortality and adverse clinical events after acute myocardial infarction (AMI), but there are evidences that the relationship of blood glucose and mortality may differ between diabetic and nondiabetic patients. Prior studies in China have provided mixed results and are limited by statistical power. Here, we used data from a large, nationally representative sample of patients hospitalized with AMI in China in 2001, 2006, and 2011 to assess if admission glucose is of prognostic value in China and if this relationship differs depending on the presence or absence of diabetes.

METHODSUsing a nationally representative sample of patients with AMI in China in 2001, 2006, and 2011, we categorized patients according to their glucose levels at admission (Results: Compared to patients with euglycemia (5.8%), patients with moderate hyperglycemia (13.1%, odds ratio [OR] = 2.44, 95% confidence interval [CI, 2.08-2.86]), severe hyperglycemia (21.5%, OR = 4.42, 95% CI [3.78-5.18]), and hypoglycemia (13.8%, OR = 2.59, 95% CI [1.68-4.00]), all had higher crude in-hospital mortality after AMI regardless of the presence of recognized diabetes mellitus. After adjustment for patients' characteristics and clinical status, however, the relationship between admission glucose and in-hospital mortality was different for diabetic and nondiabetic patients (P for interaction = 0.045). Among diabetic patients, hypoglycemia (OR = 3.02, 95% CI [1.20-7.63]), moderate hyperglycemia (OR = 1.75, 95% CI [1.04-2.92]), and severe hyperglycemia (OR = 2.97, 95% CI [1.87-4.71]) remained associated with elevated risk for mortality, but among nondiabetic patients, only patients with moderate hyperglycemia (OR = 2.34, 95% CI [1.93-2.84]) and severe hyperglycemia (OR = 3.92, 95% CI [3.04-5.04]) were at elevated mortality risk and not hypoglycemia (OR = 1.12, 95% CI [0.60-2.08]). This relationship was consistent across different study years (P for interaction = 0.900).

CONCLUSIONSThe relationship between admission glucose and in-hospital mortality differs for diabetic and nondiabetic patients. Hypoglycemia was a bad prognostic marker among diabetic patients alone. The study results could be used to guide risk assessment among AMI patients using admission glucose.

TRIAL REGISTRATIONwww.clinicaltrials.gov, NCT01624883; https://clinicaltrials.gov/ct2/show/NCT01624883.