Martha Sylvia

Case Study: Johns Hopkins Community Health Partnership: A model for transformation

To address the challenging health care needs of the population served by an urban academic medical center, we developed the Johns Hopkins Community Health Partnership (J-CHiP), a novel care coordination program that provides services in homes, community clinics, acute care hospitals, emergency departments, and skilled nursing facilities. This case study describes a comprehensive program that includes: a community-based intervention using multidisciplinary care teams that work closely with the patients primary care provider; an acute care intervention bundle with collaborative team-based care; and a skilled nursing facility intervention emphasizing standardized transitions and targeted use of care pathways. The program seeks to improve clinical care within and across settings, to address the non-clinical determinants of health, and to ultimately improve healthcare utilization and costs. The case study introduces: a) main program features including rationale, goals, intervention design, and partnership development; b) illness burden and social barriers of the population contributing to care challenges and opportunities; and c) lessons learned with steps that have been taken to engage both patients and providers more actively in the care model. Urban health systems, including academic medical centers, must continue to innovate in care delivery through programs like J-CHiP to meet the needs of their patients and communities.

AN APPROACH TO CLINICAL DATA MANAGEMENT FOR THE DOCTOR OF NURSING PRACTICE CURRICULUM

Strong data management skills are essential to doctor of nursing practice (DNP) education and necessary for DNP practice. Completion of the DNP scholarly project requires application of these skills to understand and address a complex practice, process, or systems problem; develop, implement, and monitor an innovative evidence-based intervention to address that problem; and evaluate the outcomes. The purposes of this paper were to describe the demand and context for clinical data management (CDM) within the DNP curriculum; provide an overview of CDM content; describe the process for content delivery; propose a set of course objectives; and describe initial successes and challenges. A two-pronged approach of consultation and a CDM course were developed. Students who participated in this approach were more likely to create and implement an evaluation plan; apply techniques for data cleansing and manipulation; apply concepts of sample size determination using power analysis; use exploratory data analysis techniques to understand population attributes and sampling bias; apply techniques to adjust for bias; apply statistical significance testing; and present project results in a meaningful way. On the basis of this evaluation, CDM has evolved from an elective to a required course integrated in a thread that crosses the entire curriculum.

Clinical analytics and data management for the DNP

Strong data management knowledge and skills are a requirement for every DNP graduate. This unique text focuses on fostering the rigorous, meticulous data management skills that can improve care experience, health outcomes, and cost-savings worldwide. It provides a knowledge base, describes the regulatory and ethical context, outlines a process to guide evaluation, presents a compendium of resources, and includes examples of evaluation of translation effects. It takes the DNP student step-by-step through the complete process of data management including planning, data collection, data governance and cleansing, analysis, and data presentation. Moreover, the text continues the process of establishing a sturdy clinical data management (CDM) skill base by presenting techniques for ongoing project monitoring after analysis and evaluation are concluded. A progressive case study illustrates multiple techniques and methods throughout each chapter, enabling students to apply what they have learned to their own DNP projects. The book features information from professors who are highly experienced in teaching CDM as well as a renowned scholar of cost-savings and evaluation of program outcomes. The text provides very specific examples of techniques using SPSS software that is familiar to graduate nursing students. Chapters include objectives, references, and examples from translation projects to assist students in learning and applying chapter content. Appendices describe numerous tools and tricks of the trade compiled by the authors over several years of teaching clinical data management to DNP students. Key Features:* Meets the specific data management needs of the DNP student from planning to presentation* Presents a wide selection of data display options through frequent illustrations of SPSS data* Uses a progressive case study to illustrate multiple techniques and methods throughout chapters* Provides substantial content necessary for the DNP student to execute evaluation of DNP innovations/projects* Includes very specific examples of techniques using software that is familiar to graduate nursing students

Going beyond Clinical Care to Reduce Health Care Spending

Background: Addressing both clinical and nonclinical determinants of health is essential for improving population health outcomes. In 2012, the Johns Hopkins Community Health Partnership (J-CHiP) implemented innovative population health management programs across acute and community environments. The community-based program involved multidisciplinary teams [ie, physicians, care managers (CM), health behavior specialists (HBS), community health workers, neighborhood navigators] and collaboration with community-based organizations to address social determinants. Objectives: To report the impact of a community-based program on cost and utilization from 2011 to 2016. Design: Difference-in-difference estimates were calculated for an inclusive cohort of J-CHiP participants and matched nonparticipants. The analysis was replicated for participants with a CM and/or HBS to estimate the differential impact with more intensive program services. Subjects: A total of 3268 high-risk Medicaid and Medicare beneficiaries (1634 total J-CHiP participants, 1365 with CM and 678 with HBS). Outcome Measures: Paid costs and counts of emergency department visits, admissions, and readmissions per member per year. Results: For Medicaid, costs were almost

A pilot test of the effect of guided care on the quality of primary care experiences for multimorbid older adults.

1200 per member per year lower for participants as a whole,

Guided care: cost and utilization outcomes in a pilot study.

2000 lower for those with an HBS, and

Clinical features of high-risk older persons identified by predictive modeling.

3000 lower for those with a CM; hospital admission and readmission rates were 9%–26% lower for those with a CM and/or HBS. For Medicare, costs were lower (−

Morbidity and older persons' perceptions of the quality of their primary care

476), but utilization was similar or higher than nonparticipants. None of the observed Medicaid or Medicare differences were statistically significant. Conclusions: Although not statistically significant, the results indicate a promising innovation for Medicaid beneficiaries. For Medicare, the impact was negligible, indicating the need for further program modification.