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市場調查報告書

醫療分析的重要化案例

The Case for Making Analytics a First-Class Citizen in Healthcare

出版商 Ovum, Ltd. 商品編碼 355282
出版日期 內容資訊 英文 22 Pages
商品交期: 最快1-2個工作天內
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醫療分析的重要化案例 The Case for Making Analytics a First-Class Citizen in Healthcare
出版日期: 2015年01月08日 內容資訊: 英文 22 Pages
簡介

現在在醫療中資訊的活用正扮演著無比重要的角色。但那其中也存在大量的課題。

本報告提供今後在醫療轉換上分析至關重要的理由,提供從分析得來有效考察手法,實行分析時醫療機關所應避開的陷阱等相關資料,為您概述為以下內容。

第1章 摘要

  • 概要
  • Ovum的見解
  • 主要的訊息
  • 給企業的建議
  • 給供應商的建議

第2章 必須以可行之考察改變醫療市場

  • 無論是何種立場,有效利用資訊可推動轉變
  • 不採取行動,將導致醫療資料的數位化資訊過多
  • 分析可改變市場

第3章 醫療分析領域的理解:案例,計劃,技術之相互關係

  • 幾乎全體適用的分析
  • 存在所有層級裡的分析範圍
  • 流程,實行,準備
  • 廣範圍需要結構性檢驗有效分析
  • EHR供應商的決策和分析解決方案為特定的分析使用案例帶來一定的優點

第4章 建立分析的組織:應該預先知道的事

  • 典型醫療企業分析的課題
  • 連接的案例:有形·無形
  • 組織需準備好應對分析的障礙
  • 將面臨的「來源系統的無政府狀態」:準備好應對大量的資料整合
  • 為求成功,強力的資料品質開發與管治不可或缺
  • 準備好應對變化的管理

第5章 附屬資料

  • 調查手法
  • 相關報告書
  • 著者
目錄
Product Code: IT0011-000339

The effective use of information is starting to play a much more central role in healthcare today than it has in the past. However, the path forward is not without its challenges.

Highlights

  • Although many organizations have made great strides in digitizing data via electronic health records (EHRs) and clinical systems, the healthcare information ecosystem remains fragmented. Significant value is locked up in large quantities of raw and/or unwieldy digital data, resulting in significant manual heavy lifting to run even relatively simplistic queries. Healthcare is desperately in need of actionable insights, tailored to specific user groups, which can be used to drive much-needed change across financial, organizational, and clinical domains.
  • The creation of sustainable analytics infrastructure, practices, and culture will be critical to progress. This is not an easy undertaking, and many underestimate the amount of work required "below the surface" in areas such as data cleansing, normalization, and integration. In this report we explore the context, environment, drivers, and requirements for making analytics a first-class citizen in healthcare.
  • Evaluate the different use cases and drivers for analytics investment in healthcare.
  • Learn how different healthcare organizations are effectively implementing analytics from a people, process, and technology perspective.

Features Benefits

  • Evaluate the different use cases and drivers for analytics investment in healthcare.
  • Learn how different healthcare organizations are effectively implementing analytics from a people, process, and technology perspective.

Questions Answers

  • Why is analytics so important to the future of healthcare transformation, and how can effective insights be derived from analytics?
  • What are the key pitfalls that healthcare organizations should avoid in analytics implementations?

Table of Contents

1. Summary

  • Catalyst
  • Ovum view
  • Key messages
  • Recommendations for enterprises
  • Recommendations for vendors

2. Actionable insights are a critical game-changer for healthcare

  • Effective use of information underpins transformation, regardless of which side you are on
  • Digitization of healthcare data will result in information overload, unless action is taken
  • Analytics can facilitate game-changing insights

3. Understanding the healthcare analytics domain: Use cases, planning, and technology implications

  • Analytics can be applied to almost everything
  • There is scope for analytics at every level
  • Process, executive support, and preparedness
  • Widespread, effective use of analytics will require architectural overhauls
  • EHR vendor decision support and analytics solutions have some advantages for certain analytics use cases

4. Building an analytical organization: What you need to know

  • Typical healthcare enterprise analytics challenges
  • Articulating the business case: tangibles and intangibles
  • Organizations must be prepared to tackle analytics icebergs
  • There's “anarchy of source systems” out there: prepare for significant data integration
  • Developing strong data quality and governance is critical to success
  • Prepare for change management

5. Appendix

  • Methodology
  • Further reading
  • Authors

Figures

  • Figure 1: Mainstream user experience of healthcare information
  • Figure 2: Corresponding data infrastructure
  • Figure 3: Healthcare analytics users and spectrum of use cases
  • Figure 4: Factors critical to success
  • Figure 5: The different architectural aspects to consider in healthcare
  • Figure 6: Criteria against which organizations should assess themselves
  • Figure 7: Work below the application surface
  • Figure 8: IBM Watson Analytics data anonymization
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